Disclaimer: The purpose of the Open Case Studies project is to demonstrate the use of various data science methods, tools, and software in the context of messy, real-world data. A given case study does not cover all aspects of the research process, is not claiming to be the most appropriate way to analyze a given data set, and should not be used in the context of making policy decisions without external consultation from scientific experts. ####

This work is licensed under the Creative Commons Attribution-NonCommercial 3.0 (CC BY-NC 3.0) United States License.

To cite this case study please use:

Wright, Carrie and Ontiveros, Michael and Jager, Leah and Taub, Margaret and Hicks, Stephanie. (2020). https://github.com/opencasestudies/ocs-bp-RTC-wrangling. Influence of Multicollinearity on Measured Impact of Right-to-Carry Gun Laws Part 1 (Version v1.0.0).

To access the GitHub repository for this case study see here: https://github.com/opencasestudies/ocs-bp-RTC-wrangling.
This case study is part of a series of public health case studies for the Bloomberg American Health Initiative.
See this case study for part 2 which includes a data analysis and information about data visualization.

Motivation

This case study shows the wrangling performed for another case study.

This other case study introduces the topic of multicollinearity, which occurs in regression when one or more independent variables can be predicted by other independent variables.

It does so by showcasing a real world example where multicollinearity in part resulted in historically controversial and conflicting findings about the influence of the adoption of right-to-carry (RTC) concealed handgun laws on violent crime rates in the United States.

We will focus on two articles:

  1. The first analysis by Mustard and Lott published in 1996 suggests that RTC laws reduce violent crime. Lott authored a book extending these findings in 1998 called More Guns, Less Crime.

[source]
  1. The second analysis is a recent article by Donohue, et al. published in 2017 that suggests that RTC laws increase violent crime. Donohue has also published previous articles with titles such as Shooting down the “More Guns, Less Crime” Hypothesis.

[source]

This has been a controversial topic as many other analyses also produced conflicting results. See here for a list of studies.

The Donohue, et al. article discusses how there are many other important methodological aspects besides multicollinearity (which occurs when predictor or input variables are highly related in a regression analysis) that could account for the historically conflicting results in these previous manuscripts.

In fact, nearly every aspect of the data analysis process was different between the Donohue, et al. and Mustard and Lott analyses.

However, we will focus particularly on multicollinearity and how it can influence the results we get from linear regression. Specifically, this analysis will demonstrate how methodological details can be critically influential for our overall conclusions and can result in important policy related consequences. The Donohue, et al. article will provide a basis for the motivation.

John J. Donohue et al., Right‐to‐Carry Laws and Violent Crime: A Comprehensive Assessment Using Panel Data and a State‐Level Synthetic Control Analysis. Journal of Empirical Legal Studies, 16,2 (2019).

David B. Mustard & John Lott. Crime, Deterrence, and Right-to-Carry Concealed Handguns. Coase-Sandor Institute for Law & Economics Working Paper No. 41, (1996).

Before we leave this section, we provide a high-level overview of what variables were (or were not) included in the Donohue, Aneja and Weber) (DAW) paper and the Mustard and Lott (ML) paper:

[source]
*ML is abbreviated as LM in the source article

Note: We are not attempting to re-create the analyses from the original authors. Instead, we aim to use a subset of the listed explanatory variables in this case study to demonstrate multicollinearity. These variables will be consistent for both analyses that we will perform, with the exception that one analysis will have 6 demographic variables as in the analysis in the Donohue, et al. article and the other will have 36 demographic variables, grouping individuals into more specific categories, as in the analysis in the Mustard and Lott article.

Main Question

Our main question:

What is the effect of multicollinearity on coefficient estimates from linear regression models when analyzing right to carry laws and violence rates?

Specifically, we will consider the two ways to define the demographic variables (as described above) and investigate how the inclusion of different numbers of age groups influences the results of an analysis of right to carry laws and violence rates

In this case study we only demonstrate how to import and wrangle the data.

Learning Objectives

Data Science Learning Objectives:

  1. Data import of many different file types with special cases (readr, readxl, pdftools)
  2. Joining data from multiple sources (dplyr)
  3. Working with character strings (stringr)
  4. Data comparisons (dplyr and janitor)
  5. Reshaping data into different formats (tidyr)

We will especially focus on using packages and functions from the tidyverse, such as dplyr and ggplot2. The tidyverse is a library of packages created by RStudio. While some students may be familiar with previous R programming packages, these packages make data science in R especially legible.

Context

So what exactly is a right-to-carry law?

It is a law that specifies if and how citizens are allowed to have a firearm on their person or nearby (for example, in a citizen’s car) in public.

The Second Amendment to the United States Constitution guarantees the right to “keep and bear arms”. The amendment was ratified in 1791 as part of the Bill of Rights.

[source]

However, there are no federal laws about carrying firearms in public.

These laws are created and enforced at the US state level. States vary greatly in their laws about the right to carry firearms. Some require extensive effort to obtain a permit to legally carry a firearm, while other states require very minimal effort to do so.

Click here for more information on history of right-to-carry policies in the US.

According to the Wikipedia entry about the history of right-to-carry policies in the United States:

Public perception on concealed carry vs open carry has largely flipped. In the early days of the United States, open carrying of firearms, long guns and revolvers was a common and well-accepted practice. Seeing guns carried openly was not considered to be any cause for alarm. Therefore, anyone who would carry a firearm but attempt to conceal it was considered to have something to hide, and presumed to be a criminal. For this reason, concealed carry was denounced as a detestable practice in the early days of the United States.

Concealed weapons bans were passed in Kentucky and Louisiana in 1813. (In those days open carry of weapons for self-defense was considered acceptable; concealed carry was denounced as the practice of criminals.) By 1859, Indiana, Tennessee, Virginia, Alabama, and Ohio had followed suit. By the end of the nineteenth century, similar laws were passed in places such as Texas, Florida, and Oklahoma, which protected some gun rights in their state constitutions. Before the mid 1900s, most U.S. states had passed concealed carry laws rather than banning weapons completely. Until the late 1990s, many Southern states were either “No-Issue” or “Restrictive May-Issue”. Since then, these states have largely enacted “Shall-Issue” licensing laws, with numerous states legalizing “Unrestricted concealed carry”.

There are five broad categories of right-to-carry laws:

[source]

You can see that no state in the US currently (this map is from 2020) has a “Rights Infringed/Non-Issue” law (the gray category) – meaning that all 50 states in the US allow the right to carry firearms at least in some way. However the level of restrictions is dramatically different from one state to another.

Click here for more information about how restrictions vary from one state to another.

There is variation from state to state even within the same general category:

For example here is an abridged version of the current carry laws in Idaho which is considered an “Unrestricted - no permit required” state:

State law … allows any resident of Idaho or a current member of the armed forces of the United States to carry a concealed handgun without a license to carry, provided the person is over 18 years old and not disqualified from being issued a license to carry concealed weapons under state law. An amendment to state law that takes effect on July 1, 2020 changes the reference in the above law from “a resident of Idaho” to “any citizen of the United States.”

And here are is an abridged version of the current carry laws in Arizona which is also considered an “Unrestricted - no permit required” state:

Any person 21 years of age or older, who is not prohibited possessor, may carry a weapon openly or concealed without the need for a license…

Notice that citizens in Idaho only need to be 18 to carry a firearm, whereas they must be 21 in Arizona.

Limitations

There are some important considerations regarding this data analysis to keep in mind:

  1. We do not use all of the data used by either the Mustard and Lott or Donohue, et al. analyses, nor do we perform the same analysis as in each article. We instead perform a much simpler analysis with fewer variables for the purposes of illustration of the concept of multicollinearity and its influence on regression coefficients, not to reproduce either analysis.

  2. Our analysis accounts for either the adoption or lack of adoption of a permissive right-to-carry law in each state, but does not account for differences in the level of permissiveness of the laws.

Recall that these are the categories of right to carry laws:

States with laws of the category rights restricted - very limited issue (red) are considered as not having a permissive right-to-carry law. Recall that no states currently have a rights infringed/non-issue law.

States of all other categories (shall issue, discretionary/reasonable issue, and no permit required, all shades of blue) are considered the same in our analysis, as having a permissive right-to-carry law.

  1. Because our analysis in the next case study is an oversimplification, the results presented here should not be used for determining policy changes; instead we suggest that users interested in such a determination consult with a specialist.

  2. The inclusion of race as an explanatory variable in an epidemiological study can be useful in certain circumstances. However, there are limitations and issues around defining, determining, and reporting race, as well as in interpreting differences in public health outcomes by race. For more information on this topic, we have included a link to a paper on the use of race as a measure in epidemiology. We include race in this analysis to demonstrate and consider the limitations of what the previous papers have done to analyze the influence of RTC laws on violent crime, with a focus on multicollinearity. Thus in our analysis we have also defined race as was previously done in these papers. Furthermore, we want to point out that reporting analyses about crime with race as a variable can have very unexpected consequences and thus care should be taken. See here for suggestions. Any association between demographic variables (indicating the proportion of the population from specific race and age groups) and violent crime does not necessarily indicate that the two are linked causally, as aside from the issues presented in the article, this may instead indicate higher rates of police engagement with certain racial groups due to racial profling.

The ACLU defines racial profiling as:

“Racial Profiling” refers to the discriminatory practice by law enforcement officials of targeting individuals for suspicion of crime based on the individual’s race, ethnicity, religion or national origin.

We will begin by loading the packages that we will need:

Packages used in this case study:

Package Use in this case study
here to easily load and save data
readxl to import the data in the excel files
readr to import the CSV file data
pdftools to import data from a pdf file
dplyr to arrange/filter/select/compare specific subsets of the data
magrittr to use the compound assignment pipe operator %<>%
tidyr to rearrange data in wide and long formats
stringr to manipulate the character strings within the data
purrr to import the data in all the different excel and csv files efficiently
forcats to allow for reordering of factors in plots
tibble to create data objects that we can manipulate with dplyr/stringr/tidyr/purrr

What are the data?

Below is a table from the Donohue, et al. paper that shows the data used in both analyses, where DAW stands for Donohue, et al. and ML stands for Mustard and Lott.

We will be using a subset of these variables, which are highlighted in green:

Data Import

Demographic and population data

To obtain information about age, sex, and race, and overall population we will use US Census Bureau data, just like both of the articles. The census data is available for different time spans. Here are the links for the years used in our analysis. We will use data from 1977 to 2010.

Data Link
years 1977 to 1979 link
years 1980 to 1989 link * county data was used for this decade which also has state information
years 1990 to 1999 link
years 2000 to 2010 link
technical documentation

To import the data we will use the read_csv() function of the readr package for the csv files. In some decades, there are separate files for each year, we will read each of these together using the base list.files() function to get all of the names for each file and then the map() function of the purrr package to apply the read_csv() function on all of the file paths in the list created by list.files(). For years that are txt files we will use read_table2() also for the readr package. The read_table2() function, unlike the read_table(), allows for any number of white space characters between columns, and the lines can be of different lengths.

[[1]]
# A tibble: 62,244 x 21
   REGION DIVISION STATE NAME    SEX ORIGIN  RACE AGEGRP ESTIMATESBASE20…
    <dbl>    <dbl> <dbl> <chr> <dbl>  <dbl> <dbl>  <dbl>            <dbl>
 1      0        0     0 Unit…     0      0     0      0        281424600
 2      0        0     0 Unit…     0      0     0      1         19176154
 3      0        0     0 Unit…     0      0     0      2         20549855
 4      0        0     0 Unit…     0      0     0      3         20528425
 5      0        0     0 Unit…     0      0     0      4         20218782
 6      0        0     0 Unit…     0      0     0      5         18962964
 7      0        0     0 Unit…     0      0     0      6         19381792
 8      0        0     0 Unit…     0      0     0      7         20511067
 9      0        0     0 Unit…     0      0     0      8         22707390
10      0        0     0 Unit…     0      0     0      9         22442442
# … with 62,234 more rows, and 12 more variables: POPESTIMATE2000 <dbl>,
#   POPESTIMATE2001 <dbl>, POPESTIMATE2002 <dbl>, POPESTIMATE2003 <dbl>,
#   POPESTIMATE2004 <dbl>, POPESTIMATE2005 <dbl>, POPESTIMATE2006 <dbl>,
#   POPESTIMATE2007 <dbl>, POPESTIMATE2008 <dbl>, POPESTIMATE2009 <dbl>,
#   CENSUS2010POP <dbl>, POPESTIMATE2010 <dbl>

Notice that the STATE variable for the demographic data is numeric. That is because it is encoded by Federal Information Processing Standard (FIPS) state codes{target="_blank". Thus we also need to import data about FIPS encoding so that we can identify what data corresponds to what state.

State FIPS codes

The following data was downloaded from the US Census Bureau.

To import the data we will use the read_xls() function of the readxl package. Since the first five lines of this excel is information about the source of the data and when it was released, we need to skip importing these lines using the skip argument so that the data has the same number of columns for each row.

# A tibble: 64 x 4
   Region Division `State\n(FIPS)` Name                    
   <chr>  <chr>    <chr>           <chr>                   
 1 1      0        00              Northeast Region        
 2 1      1        00              New England Division    
 3 1      1        09              Connecticut             
 4 1      1        23              Maine                   
 5 1      1        25              Massachusetts           
 6 1      1        33              New Hampshire           
 7 1      1        44              Rhode Island            
 8 1      1        50              Vermont                 
 9 1      2        00              Middle Atlantic Division
10 1      2        34              New Jersey              
# … with 54 more rows

Police staffing data

The following data was downloaded from the Federal Bureau of Investigation.

The read_csv() function of the readr package guesses what the class is for each variable, but sometimes it makes mistakes. It is good to specify the class for variables if you know them. We know that we want the variables about male and female counts to be numeric. We can specify that using the col_types = argument. See here and here for more information.

Unemployment data

The following data was downloaded from the U.S. Bureau of Labor Statistics.

There are excel files for each state. As you can see, there are many rows to skip to make sure that there are the same number of columns for each row. We can also see that the state name is located in a couple of the first rows.

We can also see that here if we just try to read in the files directly.

[[1]]
# A tibble: 55 x 14
   `Local Area Une… ...2  ...3  ...4  ...5  ...6  ...7  ...8  ...9  ...10 ...11
   <chr>            <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
 1 Original Data V… <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA> 
 2 <NA>             <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA> 
 3 Series Id:       LAUS… <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA> 
 4 Not Seasonally … <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA> 
 5 Area:            Alab… <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA> 
 6 Area Type:       Stat… <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA> 
 7 State/Region/Di… Alab… <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA> 
 8 Measure:         unem… <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA> 
 9 Years:           1977… <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA> 
10 <NA>             <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA> 
# … with 45 more rows, and 3 more variables: ...12 <chr>, ...13 <chr>,
#   ...14 <chr>

So now we will skip the first 10 lines. And also create a names tibble that contains only the cell with the state information.

[[1]]
# A tibble: 44 x 14
    Year   Jan   Feb   Mar   Apr   May   Jun   Jul   Aug   Sep   Oct   Nov   Dec
   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
 1  1977   7.5   9     7.7   7.2   6.8   8.6   8     7.8   6.7   6.3   6.3   6  
 2  1978   7.1   6.9   6.2   5.4   5.1   6.9   6.7   6.7   6.5   6.3   6.3   6.5
 3  1979   6.7   7.5   6.9   6.6   6.4   8.4   7.7   7.8   7.1   7.2   6.9   6.7
 4  1980   7.7   7.8   7.4   7.4   8.4   9.7  10.4  10.3   9.3   9.6   9.4   9  
 5  1981  10    10.3   9.5   9.1   9.4  11.1  10.4  10.9  10.8  11.7  11.5  11.8
 6  1982  13.2  13.2  12.9  12.6  12.8  14.5  14.7  14.8  14.7  15.1  15.4  15.3
 7  1983  16    16    14.5  13.7  13.3  14.6  13.9  13.8  13.2  12.8  12.1  11.8
 8  1984  12.5  12.4  11.4  10.8  10.1  11.3  11.5  11.3  10.8  10.2   9.7  10.1
 9  1985  10.7  10.5   9.8   8.7   8.4   9.6   9.2   8.8   8.6   8.6   8.4   8.7
10  1986   9.3  10.4  10.1   9.4   9.4  10.5   9.7   9.6   9.7   9.7   9.6   9  
# … with 34 more rows, and 1 more variable: Annual <dbl>

To get the state name for each file using the map() function to perform functions across all of the files, we will specifically import only a small range of cells using the range = argument and then grab the cell that has state information based on it’s location within the range of cells imported using c() and then use the base unlist() function to unlist the list that this creates.

 [1] "Alabama"              "Alaska"               "Arizona"             
 [4] "Arkansas"             "California"           "Colorado"            
 [7] "Connecticut"          "Delaware"             "District of Columbia"
[10] "Florida"              "Georgia"              "Hawaii"              
[13] "Idaho"                "Illinois"             "Indiana"             
[16] "Iowa"                 "Kansas"               "Kentucky"            
[19] "Louisiana"            "Maine"                "Maryland"            
[22] "Massachusetts"        "Michigan"             "Minnesota"           
[25] "Mississippi"          "Missouri"             "Montana"             
[28] "Nebraska"             "Nevada"               "New Hampshire"       
[31] "New Jersey"           "New Mexico"           "New York"            
[34] "North Carolina"       "North Dakota"         "Ohio"                
[37] "Oklahoma"             "Oregon"               "Pennsylvania"        
[40] "Rhode Island"         "South Carolina"       "South Dakota"        
[43] "Tennessee"            "Texas"                "Utah"                
[46] "Vermont"              "Virginia"             "Washington"          
[49] "West Virginia"        "Wisconsin"            "Wyoming"

Now we will make these values the names of the different tibbles within ue_rate_data.

Poverty data

Extracted from Table 21 from US Census Bureau Poverty Data

# A tibble: 6 x 6
  `NOTE: Number in thousa… ...2  ...3   ...4         ...5         ...6          
  <chr>                    <chr> <chr>  <chr>        <chr>        <chr>         
1 2018                     <NA>  <NA>    <NA>        <NA>          <NA>         
2 STATE                    Total Number "Standard\n… Percent      "Standard\ner…
3 Alabama                  4877  779    "65"         16           "1.3"         
4 Alaska                   720   94     "9"          13.1         "1.2"         
5 Arizona                  7241  929    "80"         12.80000000… "1.1000000000…
6 Arkansas                 2912  462    "38"         15.9         "1.3"

We can see that this will require some wrangling to make the data more usable.

Violent crime

Violent crime data was obtained from here This data is a bit trickier because of spaces and / in the column names, thus the read_lines() function of the readr package works better than the read_csv() function.

[1] "Estimated crime in Alabama"                                                                                                           
[2] "\n,,National or state crime,,,,,,,"                                                                                                   
[3] "\n,,Violent crime,,,,,,,"                                                                                                             
[4] "\nYear,Population,Violent crime total,Murder and nonnegligent Manslaughter,Legacy rape /1,Revised rape /2,Robbery,Aggravated assault,"
[5] "1977,   3690000,      15293,         524,         929,,       3572,      10268 "                                                      
[6] "1978,   3742000,      15682,         499,         954,,       3708,      10521 "

We can see that this data will also require some wrangling to make it more usable.

Right-to-carry data

This data is extracted from table in Donohue paper {target="_blank"}. We will use the function pdf_text() of the pdftools package to import the pdf document.

[1] "                                NBER WORKING PAPER SERIES\n                      RIGHT-TO-CARRY LAWS AND VIOLENT CRIME:\n              A COMPREHENSIVE ASSESSMENT USING PANEL DATA AND\n                    A STATE-LEVEL SYNTHETIC CONTROL ANALYSIS\n                                          John J. Donohue\n                                            Abhay Aneja\n                                           Kyle D. Weber\n                                         Working Paper 23510\n                                 http://www.nber.org/papers/w23510\n                      NATIONAL BUREAU OF ECONOMIC RESEARCH\n                                     1050 Massachusetts Avenue\n                                        Cambridge, MA 02138\n                                 June 2017, Revised November 2018\nPreviously circulated as \"Right-to-Carry Laws and Violent Crime: A Comprehensive Assessment\nUsing Panel Data and a State-Level Synthetic Controls Analysis.\" We thank Dan Ho, Stefano\nDellaVigna, Rob Tibshirani, Trevor Hastie, StefanWager, Jeff Strnad, and participants at the\n2011 Conference of Empirical Legal Studies (CELS), 2012 American Law and Economics\nAssociation (ALEA) Annual Meeting, 2013 Canadian Law and Economics Association (CLEA)\nAnnual Meeting, 2015 NBER Summer Institute (Crime), and the Stanford Law School faculty\nworkshop for their comments and helpful suggestions. Financial support was provided by\nStanford Law School. We are indebted to Alberto Abadie, Alexis Diamond, and Jens\nHainmueller for their work developing the synthetic control algorithm and programming the Stata\nmodule used in this paper and for their helpful comments. The authors would also like to thank\nAlex Albright, Andrew Baker, Jacob Dorn, Bhargav Gopal, Crystal Huang, Mira Korb, Haksoo\nLee, Isaac Rabbani, Akshay Rao, Vikram Rao, Henrik Sachs and Sidharth Sah who provided\nexcellent research assistance, as well as Addis O’Connor and Alex Chekholko at the Research\nComputing division of Stanford’s Information Technology Services for their technical support.\nThe views expressed herein are those of the author and do not necessarily reflect the views of the\nNational Bureau of Economic Research.\nNBER working papers are circulated for discussion and comment purposes. They have not been\npeer-reviewed or been subject to the review by the NBER Board of Directors that accompanies\nofficial NBER publications.\n© 2017 by John J. Donohue, Abhay Aneja, and Kyle D. Weber. All rights reserved. Short\nsections of text, not to exceed two paragraphs, may be quoted without explicit permission\nprovided that full credit, including © notice, is given to the source.\n"

Again, this data will also require quite a bit of wrangling.

Data Wrangling

State FIPS codes

Let’s first take a look at our state FIPS data to see if it needs any cleaning or reshaping. We should start with this data, because we will need to use it to wrangle some of the other data.

# A tibble: 6 x 4
  Region Division `State\n(FIPS)` Name                
  <chr>  <chr>    <chr>           <chr>               
1 1      0        00              Northeast Region    
2 1      1        00              New England Division
3 1      1        09              Connecticut         
4 1      1        23              Maine               
5 1      1        25              Massachusetts       
6 1      1        33              New Hampshire

We only need the last two columns, but we might want to rename them. The Name variable is vague. The variable with the FIPS code is called State\n(FIPS). To get rid of the new line in this variable name and to change the Name variable to something more informative, we will use the rename() function of the dplyr package. To use this function, we need to list the new name first followed by = and then the existing variable. We can rename multiple variables at the same time by using a comma to separate the variables we are renaming. We will use the select() function also of the dplyr package just to keep these variables, and we will filter out the rows with FIPS values of 00 with the filter() function, again also part of the dplyr package. we will specify that we want STATEFP values that are not equal to 00 by using this operator: !=. We will also use the double pipe operator %<>% of the magrittr package which allows us to use data as input and then reassign it after we perform sum functions using it.

# A tibble: 51 x 2
   STATEFP STATE        
   <chr>   <chr>        
 1 09      Connecticut  
 2 23      Maine        
 3 25      Massachusetts
 4 33      New Hampshire
 5 44      Rhode Island 
 6 50      Vermont      
 7 34      New Jersey   
 8 36      New York     
 9 42      Pennsylvania 
10 17      Illinois     
# … with 41 more rows

Demographic and population data

Click here to see detailed information about how the demographic data was wrangled

1977-1979

Now let’s take a look at our demographic data across the decades that we wish to study. If you have very wide data (meaning it has many columns), one way to view the data so that you can see all of the columns at the same time is to use the glimpse() function of the dplyr package.

Taking a look at the first decade of data, we can see that the Race/Sex Indicator contains two types of data, the race and the sex. This does not follow the tidy data philosophy, where each cell of a tibble should only contain one piece of information. Typically one might think of using the separate() function of the tidyr package to split this variable into two. However, one of the race values is Other races and since this also has a space, this makes separating this data more tricky.

Instead we will use the str_extract() function of the stringr package and the mutate() function of the dplyr package. The “mutate()” will allow us to create new variables, and “str_extract()” function will allow us to match specific patterns and pull out matches to those patterns. Therefore, if the Race/Sex Indicator value is Other races male and if we extract patterns matching either "male" or "female" which we can specify like this pattern = "male|female" then, the value will be male.

First we need to rename the Race/Sex Indicator variable to not have spaces so that it is compatible with the str_extract() function.

We also want to rename a couple of variables to be simpler and filter the data to only include the years of the data we are interested in, as well as remove some variables that we don’t need like the FIPS State Code. We can remove variables by using the select() function with a - minus sign in front of the variable we wish to remove.

Rows: 3,060
Columns: 22
$ `Year of Estimate`   <dbl> 1970, 1970, 1970, 1970, 1970, 1970, 1970, 1970, …
$ `FIPS State Code`    <chr> "01", "01", "01", "01", "01", "01", "02", "02", …
$ `State Name`         <chr> "Alabama", "Alabama", "Alabama", "Alabama", "Ala…
$ `Race/Sex Indicator` <chr> "White male", "White female", "Black male", "Bla…
$ `Under 5 years`      <dbl> 105856, 100613, 47403, 47079, 244, 250, 12382, 1…
$ `5 to 9 years`       <dbl> 120876, 115194, 55443, 54851, 255, 251, 13888, 1…
$ `10 to 14 years`     <dbl> 129091, 122352, 60427, 60065, 253, 245, 13255, 1…
$ `15 to 19 years`     <dbl> 119500, 116107, 52921, 55144, 281, 254, 11179, 9…
$ `20 to 24 years`     <dbl> 103665, 108513, 29948, 35165, 413, 331, 20237, 1…
$ `25 to 29 years`     <dbl> 86538, 88359, 19535, 23662, 239, 302, 12538, 107…
$ `30 to 34 years`     <dbl> 74452, 77595, 17196, 22021, 236, 284, 10331, 865…
$ `35 to 39 years`     <dbl> 71511, 74941, 16654, 22248, 161, 279, 9548, 7510…
$ `40 to 44 years`     <dbl> 75242, 78908, 17564, 24249, 127, 253, 8282, 6353…
$ `45 to 49 years`     <dbl> 73874, 78589, 18186, 23028, 108, 148, 6995, 5820…
$ `50 to 54 years`     <dbl> 68048, 72481, 17618, 22104, 95, 100, 5609, 4494,…
$ `55 to 59 years`     <dbl> 61071, 67699, 18118, 21909, 88, 93, 4029, 2986, …
$ `60 to 64 years`     <dbl> 52361, 61065, 16456, 20068, 69, 94, 2392, 1830, …
$ `65 to 69 years`     <dbl> 38977, 49685, 14498, 19364, 54, 73, 1292, 965, 2…
$ `70 to 74 years`     <dbl> 26767, 37227, 9541, 12509, 70, 66, 602, 496, 8, …
$ `75 to 79 years`     <dbl> 17504, 27163, 6030, 8291, 31, 52, 326, 305, 1, 5…
$ `80 to 84 years`     <dbl> 9937, 16470, 3485, 5031, 37, 30, 211, 186, 4, 5,…
$ `85 years and over`  <dbl> 5616, 10445, 2448, 4035, 76, 29, 143, 126, 19, 4…
Rows: 918
Columns: 22
$ YEAR                <dbl> 1977, 1977, 1977, 1977, 1977, 1977, 1977, 1977, 1…
$ STATE               <chr> "Alabama", "Alabama", "Alabama", "Alabama", "Alab…
$ `Under 5 years`     <dbl> 98814, 94595, 46201, 45784, 590, 621, 14316, 1353…
$ `5 to 9 years`      <dbl> 113365, 107395, 50097, 49329, 672, 660, 14621, 13…
$ `10 to 14 years`    <dbl> 123107, 116182, 54925, 53955, 677, 653, 14795, 13…
$ `15 to 19 years`    <dbl> 135343, 130433, 58468, 59926, 674, 605, 15207, 13…
$ `20 to 24 years`    <dbl> 126053, 125352, 43898, 51433, 722, 773, 20106, 16…
$ `25 to 29 years`    <dbl> 111547, 112471, 31014, 36648, 638, 835, 20444, 18…
$ `30 to 34 years`    <dbl> 100674, 101543, 22528, 26694, 571, 766, 17514, 15…
$ `35 to 39 years`    <dbl> 81038, 83369, 17473, 22213, 498, 586, 13098, 1069…
$ `40 to 44 years`    <dbl> 75042, 77793, 16446, 22146, 356, 479, 10067, 7935…
$ `45 to 49 years`    <dbl> 76296, 79753, 16578, 22576, 295, 432, 8460, 6848,…
$ `50 to 54 years`    <dbl> 74844, 81079, 17117, 23028, 206, 326, 7268, 5914,…
$ `55 to 59 years`    <dbl> 67785, 75905, 16437, 21435, 166, 213, 5398, 4485,…
$ `60 to 64 years`    <dbl> 58853, 69406, 16276, 21075, 145, 174, 3349, 2708,…
$ `65 to 69 years`    <dbl> 48848, 62430, 15837, 21126, 107, 173, 1714, 1468,…
$ `70 to 74 years`    <dbl> 34475, 50075, 11450, 16028, 90, 138, 915, 928, 22…
$ `75 to 79 years`    <dbl> 20977, 34027, 7601, 10825, 53, 106, 500, 493, 10,…
$ `80 to 84 years`    <dbl> 10831, 21483, 3896, 6272, 25, 49, 237, 268, 4, 7,…
$ `85 years and over` <dbl> 6683, 15729, 2667, 5426, 33, 41, 153, 211, 11, 6,…
$ SEX                 <chr> "male", "female", "male", "female", "male", "fema…
$ RACE                <chr> "White", "White", "Black", "Black", "Other", "Oth…

That’s looking pretty good! We also want to take all the age group variables and make one variable that is the age group name and one that is the value of the population count for that age group. To do this we will use the pivot_longer() function of the tidyr package. To use this function, we need to use the cols argument to indicate which columns we want to pivot. We also name the new variables we will create with the names_to and values_to arguments. The names_to will be the name of the variable that will identify each age group and values_to will be the name of the variable that contains the corresponding population values.

Rows: 16,524
Columns: 6
$ YEAR      <dbl> 1977, 1977, 1977, 1977, 1977, 1977, 1977, 1977, 1977, 1977,…
$ STATE     <chr> "Alabama", "Alabama", "Alabama", "Alabama", "Alabama", "Ala…
$ SEX       <chr> "male", "male", "male", "male", "male", "male", "male", "ma…
$ RACE      <chr> "White", "White", "White", "White", "White", "White", "Whit…
$ AGE_GROUP <chr> "Under 5 years", "5 to 9 years", "10 to 14 years", "15 to 1…
$ SUB_POP   <dbl> 98814, 113365, 123107, 135343, 126053, 111547, 100674, 8103…

We also want to get data about the total population for the state for each year.

To do so we can sum all the values for the SUB_POP variable that we just created. To do this we can use the group_by and summarize() functions of the dplyr package. The group_by() function specifies how we want to calculate our sum, that we would like to calculate it for each year and each state individually. Thus, all the values that have the same STATE and YEAR values will be summed together, rather than summing using all of the values in the SUB_POP variable. The .groups argument allows us to remove the grouping after we perform the calculation with summarize().

# A tibble: 153 x 3
    YEAR STATE                 TOT_POP
   <dbl> <chr>                   <dbl>
 1  1977 Alabama               3782571
 2  1977 Alaska                 397220
 3  1977 Arizona               2427296
 4  1977 Arkansas              2207195
 5  1977 California           22350332
 6  1977 Colorado              2696179
 7  1977 Connecticut           3088745
 8  1977 Delaware               594815
 9  1977 District of Columbia   681766
10  1977 Florida               8888806
# … with 143 more rows

Now we will add the population value to the demographic tibble using the left_join() function of the dplyr package. It is important that we specify how this should be done, that the YEAR and STATE variable values should match each other. This will place the dem_77_79 variables to the left of the pop_77_79 data.

# A tibble: 16,524 x 7
    YEAR STATE   SEX   RACE  AGE_GROUP      SUB_POP TOT_POP
   <dbl> <chr>   <chr> <chr> <chr>            <dbl>   <dbl>
 1  1977 Alabama male  White Under 5 years    98814 3782571
 2  1977 Alabama male  White 5 to 9 years    113365 3782571
 3  1977 Alabama male  White 10 to 14 years  123107 3782571
 4  1977 Alabama male  White 15 to 19 years  135343 3782571
 5  1977 Alabama male  White 20 to 24 years  126053 3782571
 6  1977 Alabama male  White 25 to 29 years  111547 3782571
 7  1977 Alabama male  White 30 to 34 years  100674 3782571
 8  1977 Alabama male  White 35 to 39 years   81038 3782571
 9  1977 Alabama male  White 40 to 44 years   75042 3782571
10  1977 Alabama male  White 45 to 49 years   76296 3782571
# … with 16,514 more rows

We will also calculate the percentage that each group makes up of the total population, by dividing the SUB_POP by the TOT_POP and multiplying by 100 using the mutate() function. we will also remove the other population variables.

# A tibble: 16,524 x 6
    YEAR STATE   SEX   RACE  AGE_GROUP      PERC_SUB_POP
   <dbl> <chr>   <chr> <chr> <chr>                 <dbl>
 1  1977 Alabama male  White Under 5 years          2.61
 2  1977 Alabama male  White 5 to 9 years           3.00
 3  1977 Alabama male  White 10 to 14 years         3.25
 4  1977 Alabama male  White 15 to 19 years         3.58
 5  1977 Alabama male  White 20 to 24 years         3.33
 6  1977 Alabama male  White 25 to 29 years         2.95
 7  1977 Alabama male  White 30 to 34 years         2.66
 8  1977 Alabama male  White 35 to 39 years         2.14
 9  1977 Alabama male  White 40 to 44 years         1.98
10  1977 Alabama male  White 45 to 49 years         2.02
# … with 16,514 more rows

It is important to make sure that we have the total values we would expect. We have two levels of SEX, three levels of Race, three levels of YEAR, eighteen levels of AGE_GROUP, and fifty one levels of STATE. If we multiply this together we get 16,524 which is the same as the number of rows in our final dem_77_79 data. Looks good!

Also Let’s make the values of the SEX variable capitalized so that they match the other values of the other variables like RACE etc. This will help us to keep consistent values across the different years as we wrangle the data for the other decades. To do so we will use the str_to_title() function of the stringr package. We need to use the pull() function to get the values of SEX out of dem_77_79. Once we make them capitalized they are then reassigned to the SEX variable.

1980-1989

For this decade each year is a separate tibble and they are combined as a list.

[1] "list"

So the first thing we need to do is combine each tibble of the list together. We can do that using the bind_rows() function of dplyr which appends the data together based on the presence of columns with the same name in the different tibbles. We will use the map_df() function of the purrr package to allow us to do this across each tibble in our list.

Rows: 188,460
Columns: 21
$ `Year of Estimate`            <dbl> 1980, 1980, 1980, 1980, 1980, 1980, 198…
$ `FIPS State and County Codes` <chr> "01001", "01001", "01001", "01001", "01…
$ `Race/Sex Indicator`          <chr> "White male", "White female", "Black ma…
$ `Under 5 years`               <dbl> 985, 831, 357, 346, 4, 7, 2422, 2346, 6…
$ `5 to 9 years`                <dbl> 1096, 987, 427, 395, 9, 8, 2661, 2467, …
$ `10 to 14 years`              <dbl> 1271, 1074, 395, 415, 4, 11, 2783, 2614…
$ `15 to 19 years`              <dbl> 1308, 1259, 460, 429, 10, 5, 3049, 2841…
$ `20 to 24 years`              <dbl> 972, 1006, 300, 380, 3, 3, 2423, 2428, …
$ `25 to 29 years`              <dbl> 850, 912, 240, 235, 2, 11, 2372, 2475, …
$ `30 to 34 years`              <dbl> 891, 983, 163, 196, 4, 10, 2410, 2400, …
$ `35 to 39 years`              <dbl> 942, 1015, 120, 158, 3, 12, 2101, 2202,…
$ `40 to 44 years`              <dbl> 854, 882, 133, 147, 2, 11, 1881, 1859, …
$ `45 to 49 years`              <dbl> 828, 739, 107, 154, 4, 11, 1708, 1694, …
$ `50 to 54 years`              <dbl> 631, 602, 113, 165, 1, 7, 1657, 1798, 2…
$ `55 to 59 years`              <dbl> 524, 532, 113, 150, 1, 2, 1641, 1943, 1…
$ `60 to 64 years`              <dbl> 428, 451, 126, 166, 0, 1, 1630, 1819, 1…
$ `65 to 69 years`              <dbl> 358, 417, 128, 160, 1, 0, 1503, 1729, 1…
$ `70 to 74 years`              <dbl> 242, 332, 87, 119, 0, 0, 1163, 1335, 16…
$ `75 to 79 years`              <dbl> 123, 237, 70, 94, 0, 0, 671, 906, 87, 1…
$ `80 to 84 years`              <dbl> 52, 137, 31, 57, 0, 0, 331, 527, 43, 67…
$ `85 years and over`           <dbl> 39, 86, 13, 44, 0, 1, 187, 408, 27, 65,…

Great! Now our data is all together.

Now we will wrangle the data similarly to the previous decade.

Rows: 188,460
Columns: 22
$ YEAR                          <dbl> 1980, 1980, 1980, 1980, 1980, 1980, 198…
$ `FIPS State and County Codes` <chr> "01001", "01001", "01001", "01001", "01…
$ `Under 5 years`               <dbl> 985, 831, 357, 346, 4, 7, 2422, 2346, 6…
$ `5 to 9 years`                <dbl> 1096, 987, 427, 395, 9, 8, 2661, 2467, …
$ `10 to 14 years`              <dbl> 1271, 1074, 395, 415, 4, 11, 2783, 2614…
$ `15 to 19 years`              <dbl> 1308, 1259, 460, 429, 10, 5, 3049, 2841…
$ `20 to 24 years`              <dbl> 972, 1006, 300, 380, 3, 3, 2423, 2428, …
$ `25 to 29 years`              <dbl> 850, 912, 240, 235, 2, 11, 2372, 2475, …
$ `30 to 34 years`              <dbl> 891, 983, 163, 196, 4, 10, 2410, 2400, …
$ `35 to 39 years`              <dbl> 942, 1015, 120, 158, 3, 12, 2101, 2202,…
$ `40 to 44 years`              <dbl> 854, 882, 133, 147, 2, 11, 1881, 1859, …
$ `45 to 49 years`              <dbl> 828, 739, 107, 154, 4, 11, 1708, 1694, …
$ `50 to 54 years`              <dbl> 631, 602, 113, 165, 1, 7, 1657, 1798, 2…
$ `55 to 59 years`              <dbl> 524, 532, 113, 150, 1, 2, 1641, 1943, 1…
$ `60 to 64 years`              <dbl> 428, 451, 126, 166, 0, 1, 1630, 1819, 1…
$ `65 to 69 years`              <dbl> 358, 417, 128, 160, 1, 0, 1503, 1729, 1…
$ `70 to 74 years`              <dbl> 242, 332, 87, 119, 0, 0, 1163, 1335, 16…
$ `75 to 79 years`              <dbl> 123, 237, 70, 94, 0, 0, 671, 906, 87, 1…
$ `80 to 84 years`              <dbl> 52, 137, 31, 57, 0, 0, 331, 527, 43, 67…
$ `85 years and over`           <dbl> 39, 86, 13, 44, 0, 1, 187, 408, 27, 65,…
$ SEX                           <chr> "male", "female", "male", "female", "ma…
$ RACE                          <chr> "White", "White", "Black", "Black", "Ot…

Notice that this time the state information is based on the numeric FIPS value. We want only the first two values, as the rest indicate the county. We can use the str_sub() function of the stringr package for this. We will specify that we want to start at the first position and end at the second. Just like str_extract() we need to rename this variable first so that it is compatible.

Rows: 188,460
Columns: 23
$ YEAR                <dbl> 1980, 1980, 1980, 1980, 1980, 1980, 1980, 1980, 1…
$ STATEFP_temp        <chr> "01001", "01001", "01001", "01001", "01001", "010…
$ `Under 5 years`     <dbl> 985, 831, 357, 346, 4, 7, 2422, 2346, 672, 645, 3…
$ `5 to 9 years`      <dbl> 1096, 987, 427, 395, 9, 8, 2661, 2467, 740, 680, …
$ `10 to 14 years`    <dbl> 1271, 1074, 395, 415, 4, 11, 2783, 2614, 644, 670…
$ `15 to 19 years`    <dbl> 1308, 1259, 460, 429, 10, 5, 3049, 2841, 711, 762…
$ `20 to 24 years`    <dbl> 972, 1006, 300, 380, 3, 3, 2423, 2428, 516, 601, …
$ `25 to 29 years`    <dbl> 850, 912, 240, 235, 2, 11, 2372, 2475, 414, 469, …
$ `30 to 34 years`    <dbl> 891, 983, 163, 196, 4, 10, 2410, 2400, 303, 352, …
$ `35 to 39 years`    <dbl> 942, 1015, 120, 158, 3, 12, 2101, 2202, 224, 260,…
$ `40 to 44 years`    <dbl> 854, 882, 133, 147, 2, 11, 1881, 1859, 206, 288, …
$ `45 to 49 years`    <dbl> 828, 739, 107, 154, 4, 11, 1708, 1694, 219, 236, …
$ `50 to 54 years`    <dbl> 631, 602, 113, 165, 1, 7, 1657, 1798, 203, 261, 7…
$ `55 to 59 years`    <dbl> 524, 532, 113, 150, 1, 2, 1641, 1943, 178, 219, 8…
$ `60 to 64 years`    <dbl> 428, 451, 126, 166, 0, 1, 1630, 1819, 171, 209, 8…
$ `65 to 69 years`    <dbl> 358, 417, 128, 160, 1, 0, 1503, 1729, 170, 232, 6…
$ `70 to 74 years`    <dbl> 242, 332, 87, 119, 0, 0, 1163, 1335, 164, 182, 4,…
$ `75 to 79 years`    <dbl> 123, 237, 70, 94, 0, 0, 671, 906, 87, 129, 3, 6, …
$ `80 to 84 years`    <dbl> 52, 137, 31, 57, 0, 0, 331, 527, 43, 67, 1, 2, 56…
$ `85 years and over` <dbl> 39, 86, 13, 44, 0, 1, 187, 408, 27, 65, 1, 1, 30,…
$ SEX                 <chr> "male", "female", "male", "female", "male", "fema…
$ RACE                <chr> "White", "White", "Black", "Black", "Other", "Oth…
$ STATE               <chr> "Alabama", "Alabama", "Alabama", "Alabama", "Alab…
# A tibble: 55,080 x 6
    YEAR STATE   AGE_GROUP      SEX    RACE  SUB_POP
   <dbl> <chr>   <chr>          <chr>  <chr>   <dbl>
 1  1980 Alabama 10 to 14 years female Black   50108
 2  1980 Alabama 10 to 14 years female Other     805
 3  1980 Alabama 10 to 14 years female White  109066
 4  1980 Alabama 10 to 14 years male   Black   50768
 5  1980 Alabama 10 to 14 years male   Other     826
 6  1980 Alabama 10 to 14 years male   White  115988
 7  1980 Alabama 15 to 19 years female Black   58428
 8  1980 Alabama 15 to 19 years female Other     743
 9  1980 Alabama 15 to 19 years female White  126783
10  1980 Alabama 15 to 19 years male   Black   56808
# … with 55,070 more rows
# A tibble: 55,080 x 6
    YEAR STATE   AGE_GROUP      SEX    RACE  PERC_SUB_POP
   <dbl> <chr>   <chr>          <chr>  <chr>        <dbl>
 1  1980 Alabama 10 to 14 years female Black       1.28  
 2  1980 Alabama 10 to 14 years female Other       0.0206
 3  1980 Alabama 10 to 14 years female White       2.80  
 4  1980 Alabama 10 to 14 years male   Black       1.30  
 5  1980 Alabama 10 to 14 years male   Other       0.0212
 6  1980 Alabama 10 to 14 years male   White       2.97  
 7  1980 Alabama 15 to 19 years female Black       1.50  
 8  1980 Alabama 15 to 19 years female Other       0.0191
 9  1980 Alabama 15 to 19 years female White       3.25  
10  1980 Alabama 15 to 19 years male   Black       1.46  
# … with 55,070 more rows

Just like with the data from the 70s we will also change the values for SEX to be capitalized.

Again, it is important to make sure that we have the total values we would expect. This time we have: two levels of SEX, three levels of Race, ten levels of YEAR, eighteen levels of AGE_GROUP, and fifty one levels of STATE.

If we multiply these together we get 55,080, which is the same as the number of rows of the final dem_80_89 data. Looks good!

1990-1999

Just like the 80s we need to combine the data across the files:

Rows: 43,870
Columns: 19
$ Year     <dbl> NA, 1990, 1990, 1990, 1990, 1990, 1990, 1990, 1990, 1990, 19…
$ e        <chr> NA, "01", "01", "01", "01", "01", "01", "01", "01", "01", "0…
$ Age      <dbl> NA, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16…
$ Male     <dbl> NA, 20406, 19393, 18990, 19246, 19502, 19560, 19091, 19605, …
$ Female   <dbl> NA, 19101, 18114, 18043, 17786, 18366, 18386, 18047, 18316, …
$ Male_1   <dbl> NA, 9794, 9475, 9097, 9002, 9076, 9169, 8919, 9219, 9247, 10…
$ Female_1 <dbl> NA, 9414, 9247, 8837, 8701, 8989, 9093, 8736, 9192, 9108, 97…
$ Male_2   <dbl> NA, 103, 87, 97, 94, 108, 128, 160, 178, 166, 205, 194, 179,…
$ Female_2 <dbl> NA, 90, 93, 100, 115, 114, 130, 134, 162, 155, 193, 185, 202…
$ Male_3   <dbl> NA, 192, 146, 175, 150, 168, 170, 183, 171, 136, 177, 169, 1…
$ Female_3 <dbl> NA, 170, 182, 160, 157, 178, 158, 173, 177, 185, 179, 171, 1…
$ Male_4   <dbl> NA, 223, 190, 198, 186, 190, 210, 188, 178, 182, 221, 194, 1…
$ Female_4 <dbl> NA, 220, 196, 173, 191, 190, 170, 172, 179, 173, 166, 175, 1…
$ Male_5   <dbl> NA, 47, 41, 32, 35, 36, 30, 28, 27, 29, 32, 31, 33, 34, 32, …
$ Female_5 <dbl> NA, 45, 47, 41, 30, 26, 37, 23, 35, 31, 28, 38, 22, 39, 29, …
$ Male_6   <dbl> NA, 1, 2, 1, 9, 5, 8, 2, 4, 6, 6, 0, 1, 9, 6, 7, 5, 2, 2, 4,…
$ Female_6 <dbl> NA, 8, 0, 2, 1, 4, 5, 3, 4, 4, 3, 4, 2, 2, 7, 0, 2, 2, 1, 6,…
$ Male_7   <dbl> NA, 5, 7, 2, 3, 5, 11, 2, 7, 12, 10, 7, 5, 6, 5, 6, 6, 2, 11…
$ Female_7 <dbl> NA, 5, 5, 5, 3, 14, 6, 7, 6, 3, 11, 5, 5, 7, 8, 6, 6, 7, 3, …

For this decade the column names can’t all be imported in a simple way from the table, so they need to be recoded.

Here is what the data looks like before importing:

So, first using the base colnames() function we change the names of the column names.

Rows: 43,870
Columns: 19
$ YEAR      <dbl> NA, 1990, 1990, 1990, 1990, 1990, 1990, 1990, 1990, 1990, 1…
$ STATEFP   <chr> NA, "01", "01", "01", "01", "01", "01", "01", "01", "01", "…
$ Age       <dbl> NA, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 1…
$ NH_W_M    <dbl> NA, 20406, 19393, 18990, 19246, 19502, 19560, 19091, 19605,…
$ NH_W_F    <dbl> NA, 19101, 18114, 18043, 17786, 18366, 18386, 18047, 18316,…
$ NH_B_M    <dbl> NA, 9794, 9475, 9097, 9002, 9076, 9169, 8919, 9219, 9247, 1…
$ NH_B_F    <dbl> NA, 9414, 9247, 8837, 8701, 8989, 9093, 8736, 9192, 9108, 9…
$ NH_AIAN_M <dbl> NA, 103, 87, 97, 94, 108, 128, 160, 178, 166, 205, 194, 179…
$ NH_AIAN_F <dbl> NA, 90, 93, 100, 115, 114, 130, 134, 162, 155, 193, 185, 20…
$ NH_API_M  <dbl> NA, 192, 146, 175, 150, 168, 170, 183, 171, 136, 177, 169, …
$ NH_API_F  <dbl> NA, 170, 182, 160, 157, 178, 158, 173, 177, 185, 179, 171, …
$ H_W_M     <dbl> NA, 223, 190, 198, 186, 190, 210, 188, 178, 182, 221, 194, …
$ H_W_F     <dbl> NA, 220, 196, 173, 191, 190, 170, 172, 179, 173, 166, 175, …
$ H_B_M     <dbl> NA, 47, 41, 32, 35, 36, 30, 28, 27, 29, 32, 31, 33, 34, 32,…
$ H_B_F     <dbl> NA, 45, 47, 41, 30, 26, 37, 23, 35, 31, 28, 38, 22, 39, 29,…
$ H_AIAN_M  <dbl> NA, 1, 2, 1, 9, 5, 8, 2, 4, 6, 6, 0, 1, 9, 6, 7, 5, 2, 2, 4…
$ H_AIAN_F  <dbl> NA, 8, 0, 2, 1, 4, 5, 3, 4, 4, 3, 4, 2, 2, 7, 0, 2, 2, 1, 6…
$ H_API_M   <dbl> NA, 5, 7, 2, 3, 5, 11, 2, 7, 12, 10, 7, 5, 6, 5, 6, 6, 2, 1…
$ H_API_F   <dbl> NA, 5, 5, 5, 3, 14, 6, 7, 6, 3, 11, 5, 5, 7, 8, 6, 6, 7, 3,…

Notice also that the first row is all NA values from white space in the original table for 1990, this is probably true for each year. We can check them dimensions of our table using the base dim() function. When we filter for rows where YEAR is NA, we indeed see 10 rows, which is what we would expect if we have a row like this for each of the years in the decade. We see the same if we try a different variable. Now we will test to see how large our tibble is if we drop rows with NA values using the drop_na() function of tidyr. We that indeed our dimensions only changed by ten, so there are not other rows with missing values that we might not expect. So now we will resign the dem_90_99 variable after removing these rows.

[1] 43870    19
# A tibble: 10 x 19
    YEAR STATEFP   Age NH_W_M NH_W_F NH_B_M NH_B_F NH_AIAN_M NH_AIAN_F NH_API_M
   <dbl> <chr>   <dbl>  <dbl>  <dbl>  <dbl>  <dbl>     <dbl>     <dbl>    <dbl>
 1    NA <NA>       NA     NA     NA     NA     NA        NA        NA       NA
 2    NA <NA>       NA     NA     NA     NA     NA        NA        NA       NA
 3    NA <NA>       NA     NA     NA     NA     NA        NA        NA       NA
 4    NA <NA>       NA     NA     NA     NA     NA        NA        NA       NA
 5    NA <NA>       NA     NA     NA     NA     NA        NA        NA       NA
 6    NA <NA>       NA     NA     NA     NA     NA        NA        NA       NA
 7    NA <NA>       NA     NA     NA     NA     NA        NA        NA       NA
 8    NA <NA>       NA     NA     NA     NA     NA        NA        NA       NA
 9    NA <NA>       NA     NA     NA     NA     NA        NA        NA       NA
10    NA <NA>       NA     NA     NA     NA     NA        NA        NA       NA
# … with 9 more variables: NH_API_F <dbl>, H_W_M <dbl>, H_W_F <dbl>,
#   H_B_M <dbl>, H_B_F <dbl>, H_AIAN_M <dbl>, H_AIAN_F <dbl>, H_API_M <dbl>,
#   H_API_F <dbl>
# A tibble: 10 x 19
    YEAR STATEFP   Age NH_W_M NH_W_F NH_B_M NH_B_F NH_AIAN_M NH_AIAN_F NH_API_M
   <dbl> <chr>   <dbl>  <dbl>  <dbl>  <dbl>  <dbl>     <dbl>     <dbl>    <dbl>
 1    NA <NA>       NA     NA     NA     NA     NA        NA        NA       NA
 2    NA <NA>       NA     NA     NA     NA     NA        NA        NA       NA
 3    NA <NA>       NA     NA     NA     NA     NA        NA        NA       NA
 4    NA <NA>       NA     NA     NA     NA     NA        NA        NA       NA
 5    NA <NA>       NA     NA     NA     NA     NA        NA        NA       NA
 6    NA <NA>       NA     NA     NA     NA     NA        NA        NA       NA
 7    NA <NA>       NA     NA     NA     NA     NA        NA        NA       NA
 8    NA <NA>       NA     NA     NA     NA     NA        NA        NA       NA
 9    NA <NA>       NA     NA     NA     NA     NA        NA        NA       NA
10    NA <NA>       NA     NA     NA     NA     NA        NA        NA       NA
# … with 9 more variables: NH_API_F <dbl>, H_W_M <dbl>, H_W_F <dbl>,
#   H_B_M <dbl>, H_B_F <dbl>, H_AIAN_M <dbl>, H_AIAN_F <dbl>, H_API_M <dbl>,
#   H_API_F <dbl>
# A tibble: 43,860 x 19
    YEAR STATEFP   Age NH_W_M NH_W_F NH_B_M NH_B_F NH_AIAN_M NH_AIAN_F NH_API_M
   <dbl> <chr>   <dbl>  <dbl>  <dbl>  <dbl>  <dbl>     <dbl>     <dbl>    <dbl>
 1  1990 01          0  20406  19101   9794   9414       103        90      192
 2  1990 01          1  19393  18114   9475   9247        87        93      146
 3  1990 01          2  18990  18043   9097   8837        97       100      175
 4  1990 01          3  19246  17786   9002   8701        94       115      150
 5  1990 01          4  19502  18366   9076   8989       108       114      168
 6  1990 01          5  19560  18386   9169   9093       128       130      170
 7  1990 01          6  19091  18047   8919   8736       160       134      183
 8  1990 01          7  19605  18316   9219   9192       178       162      171
 9  1990 01          8  18823  17743   9247   9108       166       155      136
10  1990 01          9  20226  19178  10194   9784       205       193      177
# … with 43,850 more rows, and 9 more variables: NH_API_F <dbl>, H_W_M <dbl>,
#   H_W_F <dbl>, H_B_M <dbl>, H_B_F <dbl>, H_AIAN_M <dbl>, H_AIAN_F <dbl>,
#   H_API_M <dbl>, H_API_F <dbl>

Then we sum across the non-hispanic and Hispanic groups because this information is not available for the other previous decades. Then we will remove the variables for the Hispanic and non-Hispanic subgroups using select().

Rows: 43,860
Columns: 11
$ YEAR    <dbl> 1990, 1990, 1990, 1990, 1990, 1990, 1990, 1990, 1990, 1990, 1…
$ STATEFP <chr> "01", "01", "01", "01", "01", "01", "01", "01", "01", "01", "…
$ Age     <dbl> 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,…
$ W_M     <dbl> 20629, 19583, 19188, 19432, 19692, 19770, 19279, 19783, 19005…
$ W_F     <dbl> 19321, 18310, 18216, 17977, 18556, 18556, 18219, 18495, 17916…
$ B_M     <dbl> 9841, 9516, 9129, 9037, 9112, 9199, 8947, 9246, 9276, 10226, …
$ B_F     <dbl> 9459, 9294, 8878, 8731, 9015, 9130, 8759, 9227, 9139, 9812, 1…
$ AIAN_M  <dbl> 104, 89, 98, 103, 113, 136, 162, 182, 172, 211, 194, 180, 209…
$ AIAN_F  <dbl> 98, 93, 102, 116, 118, 135, 137, 166, 159, 196, 189, 204, 198…
$ API_M   <dbl> 197, 153, 177, 153, 173, 181, 185, 178, 148, 187, 176, 164, 1…
$ API_F   <dbl> 175, 187, 165, 160, 192, 164, 180, 183, 188, 190, 176, 168, 1…

Looking better! We also need to add age groups like the other decades. We will take a look at the 80s data using the distinct() function of the dplyr package to see what age groups we need. We can use the base cut() function to create a new variable with mutate() called AGE_GROUP that will have a label for every change in 5 years of age. The right = FALSE argument specifies that the interval is not closed on the right, meaning that if the value is at the cut point like the Age value is 5, then it will be in the 5 to 9 years group.

We can make the labels for the AGE_GROUP variable match those of dem_77_79 but we need to pull out the values of the tibble created by distinct(). To do this we can use the pull() function from the dplyr package. Note that it is important to check that the AGE_GROUP values are listed in order for dem_77_79. We will also remove the Age variable after we create the new AGE_GROUP variable for the dem_90_99 data.

# A tibble: 18 x 1
   AGE_GROUP        
   <chr>            
 1 Under 5 years    
 2 5 to 9 years     
 3 10 to 14 years   
 4 15 to 19 years   
 5 20 to 24 years   
 6 25 to 29 years   
 7 30 to 34 years   
 8 35 to 39 years   
 9 40 to 44 years   
10 45 to 49 years   
11 50 to 54 years   
12 55 to 59 years   
13 60 to 64 years   
14 65 to 69 years   
15 70 to 74 years   
16 75 to 79 years   
17 80 to 84 years   
18 85 years and over
 [1] "Under 5 years"     "5 to 9 years"      "10 to 14 years"   
 [4] "15 to 19 years"    "20 to 24 years"    "25 to 29 years"   
 [7] "30 to 34 years"    "35 to 39 years"    "40 to 44 years"   
[10] "45 to 49 years"    "50 to 54 years"    "55 to 59 years"   
[13] "60 to 64 years"    "65 to 69 years"    "70 to 74 years"   
[16] "75 to 79 years"    "80 to 84 years"    "85 years and over"
Rows: 43,860
Columns: 11
$ YEAR      <dbl> 1990, 1990, 1990, 1990, 1990, 1990, 1990, 1990, 1990, 1990,…
$ STATEFP   <chr> "01", "01", "01", "01", "01", "01", "01", "01", "01", "01",…
$ W_M       <dbl> 20629, 19583, 19188, 19432, 19692, 19770, 19279, 19783, 190…
$ W_F       <dbl> 19321, 18310, 18216, 17977, 18556, 18556, 18219, 18495, 179…
$ B_M       <dbl> 9841, 9516, 9129, 9037, 9112, 9199, 8947, 9246, 9276, 10226…
$ B_F       <dbl> 9459, 9294, 8878, 8731, 9015, 9130, 8759, 9227, 9139, 9812,…
$ AIAN_M    <dbl> 104, 89, 98, 103, 113, 136, 162, 182, 172, 211, 194, 180, 2…
$ AIAN_F    <dbl> 98, 93, 102, 116, 118, 135, 137, 166, 159, 196, 189, 204, 1…
$ API_M     <dbl> 197, 153, 177, 153, 173, 181, 185, 178, 148, 187, 176, 164,…
$ API_F     <dbl> 175, 187, 165, 160, 192, 164, 180, 183, 188, 190, 176, 168,…
$ AGE_GROUP <fct> Under 5 years, Under 5 years, Under 5 years, Under 5 years,…

Like the previous decades we will create a RACE and SUB_POP variable using pivot_longer() to create a single Race variable out of all the subgroup variables.

Now we need to collapse the data for the various races so that it matches the previous decades. This time we will use the case_when() function of the dplyr package and the str_detect() function of the stringr package to identify when the race is something other than B or W and replace with the value Other. The value to the right of the ~ indicates what we want the value of the new variable to be if the value of the variable we are using with str_decect() matches the condition specified. If the value does not match the specified condition, than the other values will be what ever is listed after TRUE ~. We will then create population counts as we did previously for the other decades.

Finally, we will create new sums for the sub-populations where we sum across the two Other subgroups Race to a create a single value for each value of YEAR, SEX, AGE_GROUP, and STATE by using the group_by() function and summarie().

# A tibble: 55,080 x 6
    YEAR STATE   AGE_GROUP     SEX    RACE  PERC_SUB_POP
   <dbl> <chr>   <fct>         <chr>  <chr>        <dbl>
 1  1990 Alabama Under 5 years Female Black       1.12  
 2  1990 Alabama Under 5 years Female Other       0.0347
 3  1990 Alabama Under 5 years Female White       2.28  
 4  1990 Alabama Under 5 years Male   Black       1.15  
 5  1990 Alabama Under 5 years Male   Other       0.0336
 6  1990 Alabama Under 5 years Male   White       2.43  
 7  1990 Alabama 5 to 9 years  Female Black       1.14  
 8  1990 Alabama 5 to 9 years  Female Other       0.0419
 9  1990 Alabama 5 to 9 years  Female White       2.29  
10  1990 Alabama 5 to 9 years  Male   Black       1.16  
# … with 55,070 more rows

Again, we should check to make sure that we have the total values we would expect. We have the same number of unique values for each of our variables as in with the data from the 80s, so if we collapsed the data for the different additional sub-populations in this data, then we have done it correctly.

Indeed it looks like we have 55,080 rows, which is what we would expect and is the same as the number of rows of the final dem_80_89 data. Looks good!

2000-2010

Again, for this decade we need to combine the data across years.

Rows: 62,244
Columns: 21
$ REGION            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ DIVISION          <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ STATE             <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ NAME              <chr> "United States", "United States", "United States", …
$ SEX               <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ORIGIN            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ RACE              <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ AGEGRP            <dbl> 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 1…
$ ESTIMATESBASE2000 <dbl> 281424600, 19176154, 20549855, 20528425, 20218782, …
$ POPESTIMATE2000   <dbl> 282162411, 19178293, 20463852, 20637696, 20294955, …
$ POPESTIMATE2001   <dbl> 284968955, 19298217, 20173362, 20978678, 20456284, …
$ POPESTIMATE2002   <dbl> 287625193, 19429192, 19872417, 21261421, 20610370, …
$ POPESTIMATE2003   <dbl> 290107933, 19592446, 19620851, 21415353, 20797166, …
$ POPESTIMATE2004   <dbl> 292805298, 19785885, 19454237, 21411680, 21102552, …
$ POPESTIMATE2005   <dbl> 295516599, 19917400, 19389067, 21212579, 21486214, …
$ POPESTIMATE2006   <dbl> 298379912, 19938883, 19544688, 21033138, 21807709, …
$ POPESTIMATE2007   <dbl> 301231207, 20125962, 19714611, 20841042, 22067816, …
$ POPESTIMATE2008   <dbl> 304093966, 20271127, 19929602, 20706655, 22210880, …
$ POPESTIMATE2009   <dbl> 306771529, 20244518, 20182499, 20660564, 22192810, …
$ CENSUS2010POP     <dbl> 308745538, 20201362, 20348657, 20677194, 22040343, …
$ POPESTIMATE2010   <dbl> 309349689, 20200529, 20382409, 20694011, 21959087, …

OK, the data looks a bit different from the others. First we will remove a couple of variables that we probably don’t need. Also it looks like we have some values for the entire United Sates and we will drop these to be like the other decades.

We can see that there are lots of values that are zero. According to the technical documentation for this data, zero values indicate the total for the other categories of Sex, Origin, Race, and AGEGRP.

So we will drop the total values for SEX, RACE, and AGEGRP by removing the rows where these variables are equal to zero.

We will also want to only select for the total values for Origin as we do not wish to divide the data into subgroups about Hispanic ethnicity because we do not have that information for the first two decades. Thus we will filter for only the rows where Origin is equal to zero.

We will also then remove the REGION, Division, STATE, and Origin variables. We will then rename NAME to be STATE and rename AGEGRP to be like the other decades as AGE_GROUP.

# A tibble: 11,016 x 15
   STATE   SEX  RACE AGE_GROUP POPESTIMATE2000 POPESTIMATE2001 POPESTIMATE2002
   <chr> <dbl> <dbl>     <dbl>           <dbl>           <dbl>           <dbl>
 1 Alab…     1     1         1           99527           99985           99578
 2 Alab…     1     1         2          104423          102518          101023
 3 Alab…     1     1         3          108325          108412          108059
 4 Alab…     1     1         4          108638          107370          107337
 5 Alab…     1     1         5          104337          107230          108195
 6 Alab…     1     1         6          106491          101466           98949
 7 Alab…     1     1         7          110116          110630          110416
 8 Alab…     1     1         8          123719          120283          116502
 9 Alab…     1     1         9          124961          125443          124751
10 Alab…     1     1        10          115024          117010          119354
# … with 11,006 more rows, and 8 more variables: POPESTIMATE2003 <dbl>,
#   POPESTIMATE2004 <dbl>, POPESTIMATE2005 <dbl>, POPESTIMATE2006 <dbl>,
#   POPESTIMATE2007 <dbl>, POPESTIMATE2008 <dbl>, POPESTIMATE2009 <dbl>,
#   POPESTIMATE2010 <dbl>

Now we need to recode the numeric values to the values in the technical documentation. We can do so by adding labels to each numeric level using the base function factor().

Rows: 11,016
Columns: 15
$ STATE           <chr> "Alabama", "Alabama", "Alabama", "Alabama", "Alabama"…
$ SEX             <fct> Male, Male, Male, Male, Male, Male, Male, Male, Male,…
$ RACE            <fct> White, White, White, White, White, White, White, Whit…
$ AGE_GROUP       <fct> Under 5 years, 5 to 9 years, 10 to 14 years, 15 to 19…
$ POPESTIMATE2000 <dbl> 99527, 104423, 108325, 108638, 104337, 106491, 110116…
$ POPESTIMATE2001 <dbl> 99985, 102518, 108412, 107370, 107230, 101466, 110630…
$ POPESTIMATE2002 <dbl> 99578, 101023, 108059, 107337, 108195, 98949, 110416,…
$ POPESTIMATE2003 <dbl> 99627, 99920, 108026, 107749, 109360, 98276, 109893, …
$ POPESTIMATE2004 <dbl> 99788, 99306, 107627, 108666, 109037, 98742, 107653, …
$ POPESTIMATE2005 <dbl> 100316, 99754, 106570, 110278, 108727, 100327, 105151…
$ POPESTIMATE2006 <dbl> 100820, 101251, 106228, 111640, 108847, 103869, 10161…
$ POPESTIMATE2007 <dbl> 101766, 101985, 106243, 112353, 109496, 105175, 99917…
$ POPESTIMATE2008 <dbl> 102304, 102479, 106155, 113305, 110007, 106348, 99921…
$ POPESTIMATE2009 <dbl> 101411, 102688, 106130, 113741, 111167, 106497, 10138…
$ POPESTIMATE2010 <dbl> 99480, 102939, 106324, 112272, 112423, 106593, 102923…

OK, we also want to change the shape of the data so that we have a YEAR variable and each estimate of the population is a value in a new variable called SUB_POP_temp.

We will now clean up the YEAR variable to only be the numeric value by keeping only the last 4 values of each string using the str_sub() function of the stringr package.

Now we will collapse the data for the different RACES and calculate a new SUB_POP value.

Again, the dimensions look as we expect with 60,588 rows. This time we have two levels of SEX, three levels of Race, 11 levels of YEAR, eighteen levels of AGE_GROUP, and fifty one levels of STATE. If we multiply this together we get 16,588. Looks good!

Now we will calculate the total population and percent of the total as we have done with the previous decades.

We can also check that our wrangling was performed correctly by summing the values for the individual sub-populations percentages and seeing if it totals to 100.

# A tibble: 1 x 2
  poss_error     n
  <lgl>      <int>
1 FALSE        561

Looks like the percentages for each state for each year all add up to 100, as we would expect. Great! Now we will reassign the dem_00_10 data with this processing.

# A tibble: 60,588 x 6
    YEAR AGE_GROUP     STATE   SEX    RACE  PERC_SUB_POP
   <dbl> <fct>         <chr>   <fct>  <fct>        <dbl>
 1  2000 Under 5 years Alabama Male   White       2.24  
 2  2000 Under 5 years Alabama Male   Black       1.05  
 3  2000 Under 5 years Alabama Male   Other       0.101 
 4  2000 Under 5 years Alabama Female White       2.12  
 5  2000 Under 5 years Alabama Female Black       1.03  
 6  2000 Under 5 years Alabama Female Other       0.0995
 7  2000 Under 5 years Alaska  Male   White       2.35  
 8  2000 Under 5 years Alaska  Male   Black       0.165 
 9  2000 Under 5 years Alaska  Male   Other       1.37  
10  2000 Under 5 years Alaska  Female White       2.26  
# … with 60,578 more rows

OK, now we are ready to combine all of our demographic data together!

Combining demographic data

We can check that the column names are the same for the data for each of the decades by using the setequal() function of the dplyr package.

[1] TRUE
[1] TRUE
[1] TRUE

We can also confirm that we have the same number of age groups for each decade by using the base length() function. If you did not take a look at the wrangling for the demographic data then you may be unfamiliar with the pull() function of the dplyr package. This allows you to grab the values of a variable from a tibble. The distinct() function which is also of the dplyr package creates a tibble of the unique values for a variable.

[1] 18
[1] 18
[1] 18
[1] 18

Looks good!

Now we will combine the data using the bind_rows() function of the dplyr package. This function appends the data together based on the presence of columns with the same name in the different tibbles.

Rows: 187,272
Columns: 6
$ YEAR         <dbl> 1977, 1977, 1977, 1977, 1977, 1977, 1977, 1977, 1977, 19…
$ STATE        <chr> "Alabama", "Alabama", "Alabama", "Alabama", "Alabama", "…
$ SEX          <chr> "Male", "Male", "Male", "Male", "Male", "Male", "Male", …
$ RACE         <chr> "White", "White", "White", "White", "White", "White", "W…
$ AGE_GROUP    <chr> "Under 5 years", "5 to 9 years", "10 to 14 years", "15 t…
$ PERC_SUB_POP <dbl> 2.6123502, 2.9970356, 3.2545853, 3.5780690, 3.3324688, 2…

Great! now we have a really large single tibble.

Now we want to select similar demographic data to what was used in the previous analyses.

Here is the table from the Donohue paper that compares the data used in the analyses.

We can see that only the percentage of males that were from age 15-39 of the race groups (black, white, and other) were used in the Donohue analysis.

Ultimately we intend to make a tibble of data that is similar to each analysis. Therefore, we will create a data tibble about the demographic data for each analysis now.

To do so we will first create a vector of the age groups that should be included in the Donohue-like analysis, that we will call DONOHUE_AGE_GROUPS. We will then filter for only the age groups in this vector by using the filter() function of the dplyr package and the %in% operator to indicate that we want to keep all AGE_GROUP values that are equal to those within DONOHUE_AGE_GROUPS. We also want to filter for only population percentages for males by using the == operator. Then we can collapse the age groups from 20-39 by using the fct_collpase() function of the forcats package.

# A tibble: 26,010 x 6
    YEAR STATE   SEX   RACE  AGE_GROUP      PERC_SUB_POP
   <dbl> <chr>   <chr> <chr> <fct>                 <dbl>
 1  1977 Alabama Male  White 15 to 19 years        3.58 
 2  1977 Alabama Male  White 20 to 39 years        3.33 
 3  1977 Alabama Male  White 20 to 39 years        2.95 
 4  1977 Alabama Male  White 20 to 39 years        2.66 
 5  1977 Alabama Male  White 20 to 39 years        2.14 
 6  1977 Alabama Male  Black 15 to 19 years        1.55 
 7  1977 Alabama Male  Black 20 to 39 years        1.16 
 8  1977 Alabama Male  Black 20 to 39 years        0.820
 9  1977 Alabama Male  Black 20 to 39 years        0.596
10  1977 Alabama Male  Black 20 to 39 years        0.462
# … with 26,000 more rows

We also want to create a new variable that will contain all the demographic information for each percentage just as was done in the Donohue, et al. analysis. This should result in 6 different demographic variables.

To do this we will modify the AGE_GROUP variable by using the mutate() function of the dplyr package. We will replace the spaces in the now two age group categories with and underscore using the str_replace_all() function of the stringr package which replaces all instances of a pattern in a character string.

Then we will use the group_by() function and the summarize() function also of the dplyr package to allow us to calculate a sum of the percentages for each of the sub-population percentages for the newly modified age groups in AGE_GROUP. The .groups = "drop" argument allows for the grouping to be removed after the summarize() function.

# A tibble: 10,404 x 6
    YEAR STATE   RACE  SEX   AGE_GROUP      PERC_SUB_POP
   <dbl> <chr>   <chr> <chr> <chr>                 <dbl>
 1  1977 Alabama Black Male  15_to_19_years       1.55  
 2  1977 Alabama Black Male  20_to_39_years       3.04  
 3  1977 Alabama Other Male  15_to_19_years       0.0178
 4  1977 Alabama Other Male  20_to_39_years       0.0642
 5  1977 Alabama White Male  15_to_19_years       3.58  
 6  1977 Alabama White Male  20_to_39_years      11.1   
 7  1977 Alaska  Black Male  15_to_19_years       0.163 
 8  1977 Alaska  Black Male  20_to_39_years       0.968 
 9  1977 Alaska  Other Male  15_to_19_years       1.12  
10  1977 Alaska  Other Male  20_to_39_years       2.73  
# … with 10,394 more rows

Now we will combine the variables RACE, SEX, and AGE_GROUP together into one string separated by underscores using the unite function of the tidyr package. we will call this new variable VARIABLE. We will rename the PERC_SUB_POP variable to be VALUE using the rename() function of the dplyr package. The new name should be listed first before the =.

# A tibble: 10,404 x 4
    YEAR STATE   VARIABLE                    VALUE
   <dbl> <chr>   <chr>                       <dbl>
 1  1977 Alabama Black_Male_15_to_19_years  1.55  
 2  1977 Alabama Black_Male_20_to_39_years  3.04  
 3  1977 Alabama Other_Male_15_to_19_years  0.0178
 4  1977 Alabama Other_Male_20_to_39_years  0.0642
 5  1977 Alabama White_Male_15_to_19_years  3.58  
 6  1977 Alabama White_Male_20_to_39_years 11.1   
 7  1977 Alaska  Black_Male_15_to_19_years  0.163 
 8  1977 Alaska  Black_Male_20_to_39_years  0.968 
 9  1977 Alaska  Other_Male_15_to_19_years  1.12  
10  1977 Alaska  Other_Male_20_to_39_years  2.73  
# … with 10,394 more rows

Let’s do a quick row number check. We have six different demographic variables, 51 states (DC counts as a state in this case), and 34 different years from 1977 to 2010, we should have 10,404 rows, which we do!

Now, let’s do the same for the “Lott-like” analysis.

So, in this analysis there were 36 variables covering percentages of individuals from 10 to over 65, three race groups and both males and females. This table is misprinted and does not include the word “Other” for the third race group that was used.

First we will filter out the age groups that were not included. Then we will collapse the age groups to those that were used by Mustard and Lott again using the fct_collpase() function of the forcats package.

Also we will again combine the values across the variables to create a new demographic variable with 36 levels.

We can indeed check that we have the correct number of levels for VARIABLE using the distinct() function.

# A tibble: 36 x 1
   VARIABLE                      
   <chr>                         
 1 Black_Female_10_to_19_years   
 2 Black_Female_20_to_29_years   
 3 Black_Female_30_to_39_years   
 4 Black_Female_40_to_49_years   
 5 Black_Female_50_to_64_years   
 6 Black_Female_65_years_and_over
 7 Black_Male_10_to_19_years     
 8 Black_Male_20_to_29_years     
 9 Black_Male_30_to_39_years     
10 Black_Male_40_to_49_years     
# … with 26 more rows

Combining population data

We also have population data for each decade that came from wrangling the demographic data.

We again want to combine this data, so let’s again make sure that all the different tibbles have the same column names.

[1] TRUE
[1] TRUE
[1] TRUE
# A tibble: 6 x 3
   YEAR STATE       TOT_POP
  <dbl> <chr>         <dbl>
1  1977 Alabama     3782571
2  1977 Alaska       397220
3  1977 Arizona     2427296
4  1977 Arkansas    2207195
5  1977 California 22350332
6  1977 Colorado    2696179
# A tibble: 6 x 3
   YEAR STATE       TOT_POP
  <dbl> <chr>         <dbl>
1  1980 Alabama     3899671
2  1980 Alaska       404680
3  1980 Arizona     2735840
4  1980 Arkansas    2288809
5  1980 California 23792840
6  1980 Colorado    2909545
# A tibble: 6 x 3
   YEAR STATE       TOT_POP
  <dbl> <chr>         <dbl>
1  1990 Alabama     4048508
2  1990 Alaska       553120
3  1990 Arizona     3679056
4  1990 Arkansas    2354343
5  1990 California 29950111
6  1990 Colorado    3303862
# A tibble: 6 x 3
   YEAR STATE       TOT_POP
  <dbl> <chr>         <dbl>
1  2000 Alabama     4452173
2  2000 Alaska       627963
3  2000 Arizona     5160586
4  2000 Arkansas    2678588
5  2000 California 33987977
6  2000 Colorado    4326921

Looks good!

We could check that we have 51 values for each year by using the count() function of the dplyr package.

# A tibble: 34 x 2
    YEAR     n
   <dbl> <int>
 1  1977    51
 2  1978    51
 3  1979    51
 4  1980    51
 5  1981    51
 6  1982    51
 7  1983    51
 8  1984    51
 9  1985    51
10  1986    51
# … with 24 more rows

Police staffing

Click here to see details about how the police staffing data was wrangled.

OK, now we will wrangle the police staffing data. We want to limit the data to only the years of interest. Then we will also replace NA values with zero for the male_total_ct and female_total_ct variables using the replace_na() function of the tidyr package. We will also, use the across() function of the dplyr package to select and mutate both of these columns in this way. Since both of these variables have total_ct in the name and no other variables do, we can use the contains() function of the dplyr package to specify that we want to use these columns instead of listing both out.

Rows: 1,439,467
Columns: 21
$ data_year             <dbl> 1960, 1960, 1960, 1960, 1960, 1960, 1960, 1960,…
$ ori                   <chr> "AK020045Y", "AL0011000", "AL0160600", "AL01900…
$ pub_agency_name       <chr> "Alcohol Beverage Control Board", "Homewood", "…
$ pub_agency_unit       <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ state_abbr            <chr> "AK", "AL", "AL", "AL", "AL", "AL", "AL", "AL",…
$ division_name         <chr> "Pacific", "East South Central", "East South Ce…
$ region_name           <chr> "West", "South", "South", "South", "South", "So…
$ county_name           <chr> "N/A", "JEFFERSON", "N/A", "COFFEE", "N/A", "HA…
$ agency_type_name      <chr> "Other State Agency", "City", "City", "County",…
$ population_group_desc <chr> "Cities under 2,500", "Cities from 10,000 thru …
$ population            <dbl> 0, 20289, 0, 14852, 0, 3081, 0, 18739, 2776, 0,…
$ male_officer_ct       <dbl> NA, 17, NA, 0, NA, 0, NA, 0, 0, NA, NA, 0, NA, …
$ male_civilian_ct      <dbl> NA, 3, NA, 0, NA, 0, NA, 0, 0, NA, NA, 0, NA, N…
$ male_total_ct         <dbl> NA, 20, NA, 0, NA, 0, NA, 0, 0, NA, NA, 0, NA, …
$ female_officer_ct     <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ female_civilian_ct    <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ female_total_ct       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ officer_ct            <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ civilian_ct           <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ total_pe_ct           <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ pe_ct_per_1000        <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
Rows: 932,063
Columns: 21
$ data_year             <dbl> 1977, 1977, 1977, 1977, 1977, 1977, 1977, 1977,…
$ ori                   <chr> "AK0012000", "AK0012300", "AKASP0000", "AL00125…
$ pub_agency_name       <chr> "Soldotna", "Kenai", "Alaska State Troopers", "…
$ pub_agency_unit       <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ state_abbr            <chr> "AK", "AK", "AK", "AL", "AL", "AL", "AL", "AL",…
$ division_name         <chr> "Pacific", "Pacific", "Pacific", "East South Ce…
$ region_name           <chr> "West", "West", "West", "South", "South", "Sout…
$ county_name           <chr> "KENAI PENINSULA", "KENAI PENINSULA", "N/A", "J…
$ agency_type_name      <chr> "City", "City", "State Police", "City", "Other"…
$ population_group_desc <chr> "Cities under 2,500", "Cities from 2,500 thru 9…
$ population            <dbl> 2131, 5800, 172397, 1000, 0, 8611, 2850, 975, 2…
$ male_officer_ct       <dbl> 6, 10, 0, 3, NA, 16, 6, 2, 5, NA, NA, 3, 26, 5,…
$ male_civilian_ct      <dbl> 6, 0, 0, 0, NA, 5, 0, 0, 0, NA, NA, 0, 0, 0, 4,…
$ male_total_ct         <dbl> 12, 10, 0, 3, 0, 21, 6, 2, 5, 0, 0, 3, 26, 5, 6…
$ female_officer_ct     <lgl> FALSE, FALSE, NA, FALSE, NA, FALSE, FALSE, FALS…
$ female_civilian_ct    <lgl> TRUE, NA, NA, FALSE, NA, FALSE, FALSE, FALSE, F…
$ female_total_ct       <dbl> 1, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 13, 0…
$ officer_ct            <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ civilian_ct           <lgl> NA, NA, NA, FALSE, NA, NA, FALSE, FALSE, FALSE,…
$ total_pe_ct           <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ pe_ct_per_1000        <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…

Now we can create a new variable called officer_total which will be the sum of these variables. We will then keep just this variable as well as the data_year, pub_agency_name, and state_abbr.

# A tibble: 932,063 x 4
   data_year pub_agency_name                            state_abbr officer_total
       <dbl> <chr>                                      <chr>              <dbl>
 1      1977 Soldotna                                   AK                    13
 2      1977 Kenai                                      AK                    15
 3      1977 Alaska State Troopers                      AK                     0
 4      1977 Trafford                                   AL                     3
 5      1977 Trussville Fire Department Fire and Explo… AL                     0
 6      1977 Atmore                                     AL                    21
 7      1977 East Brewton                               AL                     6
 8      1977 Brilliant                                  AL                     2
 9      1977 Camden                                     AL                     5
10      1977 Drug Enforcement Administration, Birmingh… AL                     0
# … with 932,053 more rows

Now we also want to get collapse by pub_agency_name to get a total count for each year and each state. So we will do this by using the group_by() function and grouping by data_year and state_abbr and using the summarize() function to calculate a sum.

# A tibble: 2,242 x 3
   data_year state_abbr officer_state_total
       <dbl> <chr>                    <dbl>
 1      1977 AK                         544
 2      1977 AL                        7380
 3      1977 AR                        3344
 4      1977 AS                           0
 5      1977 AZ                        6414
 6      1977 CA                       65596
 7      1977 CO                        7337
 8      1977 CT                        6051
 9      1977 CZ                           0
10      1977 DC                        4751
# … with 2,232 more rows

And we will check that we have same number of values (the number of years included in the data) for each state.

# A tibble: 6 x 2
  state_abbr     n
  <chr>      <int>
1 AK            38
2 AL            38
3 AR            38
4 AS            38
5 AZ            38
6 CA            38
[1] 0 2

Looks like all the states have 38 values.

Notice also that there are some unusual abbreviations in the state_abbr variable.

We will remove data for US terroitories and associated states

Abbreviation Territory and associated states
AS American Samoa
GM Guam
CZ Canal Zone
FS ??Federated States of Micronesia (usually FM)
MP Northern Mariana Islands
OT ??U.S. Minor Outlying Islands (usually UM)
PR Puerto Rico
VI Virgin Islands

Within the datasets package that is loaded with R, there is a data set called state that contains an object called state.abb that has the state abbreviations and state.name that has the state names. We will combine these now to add the state names to our data.

# A tibble: 6 x 2
  state_abbr STATE     
  <chr>      <chr>     
1 AL         Alabama   
2 AK         Alaska    
3 AZ         Arizona   
4 AR         Arkansas  
5 CA         California
6 CO         Colorado

One unusual thing about this data is that NE is used for Nebraska to avoid confusions with NB in Canada. So we want to replace that using the str_replace() function of the stringr package

# A tibble: 50 x 2
   state_abbr STATE         
   <chr>      <chr>         
 1 AL         Alabama       
 2 AK         Alaska        
 3 AZ         Arizona       
 4 AR         Arkansas      
 5 CA         California    
 6 CO         Colorado      
 7 CT         Connecticut   
 8 DE         Delaware      
 9 FL         Florida       
10 GA         Georgia       
11 HI         Hawaii        
12 ID         Idaho         
13 IL         Illinois      
14 IN         Indiana       
15 IA         Iowa          
16 KS         Kansas        
17 KY         Kentucky      
18 LA         Louisiana     
19 ME         Maine         
20 MD         Maryland      
21 MA         Massachusetts 
22 MI         Michigan      
23 MN         Minnesota     
24 MS         Mississippi   
25 MO         Missouri      
26 MT         Montana       
27 NB         Nebraska      
28 NV         Nevada        
29 NH         New Hampshire 
30 NJ         New Jersey    
31 NM         New Mexico    
32 NY         New York      
33 NC         North Carolina
34 ND         North Dakota  
35 OH         Ohio          
36 OK         Oklahoma      
37 OR         Oregon        
38 PA         Pennsylvania  
39 RI         Rhode Island  
40 SC         South Carolina
41 SD         South Dakota  
42 TN         Tennessee     
43 TX         Texas         
44 UT         Utah          
45 VT         Vermont       
46 VA         Virginia      
47 WA         Washington    
48 WV         West Virginia 
49 WI         Wisconsin     
50 WY         Wyoming

We need to add DC to this. We will use the add_row() function of dplyr to do this. We just need to specify values for both of the variables.

Now we will add this to our police staffing data and then remove the state_abbr variable, so that we just have state names. We will also

# A tibble: 1,938 x 3
   data_year officer_state_total STATE               
       <dbl>               <dbl> <chr>               
 1      1977                 544 Alaska              
 2      1977                7380 Alabama             
 3      1977                3344 Arkansas            
 4      1977                6414 Arizona             
 5      1977               65596 California          
 6      1977                7337 Colorado            
 7      1977                6051 Connecticut         
 8      1977                4751 District of Columbia
 9      1977                1018 Delaware            
10      1977               24588 Florida             
# … with 1,928 more rows

Now we will rename the variables to match those of the other datasets.

# A tibble: 1,938 x 4
    YEAR VALUE STATE                VARIABLE           
   <dbl> <dbl> <chr>                <chr>              
 1  1977   544 Alaska               officer_state_total
 2  1977  7380 Alabama              officer_state_total
 3  1977  3344 Arkansas             officer_state_total
 4  1977  6414 Arizona              officer_state_total
 5  1977 65596 California           officer_state_total
 6  1977  7337 Colorado             officer_state_total
 7  1977  6051 Connecticut          officer_state_total
 8  1977  4751 District of Columbia officer_state_total
 9  1977  1018 Delaware             officer_state_total
10  1977 24588 Florida              officer_state_total
# … with 1,928 more rows

We also need to adjust the value to be that of every 100,000 people in the state. To do so we need the population for each state, which luckily we already have. We will slightly modify the population data and create a new tibble that will make it more clear how we are dividing by it.

# A tibble: 6 x 3
   YEAR STATE      Population_temp
  <dbl> <chr>                <dbl>
1  1977 Alabama            3782571
2  1977 Alaska              397220
3  1977 Arizona            2427296
4  1977 Arkansas           2207195
5  1977 California        22350332
6  1977 Colorado           2696179
# A tibble: 6 x 5
   YEAR VALUE STATE      VARIABLE            Population_temp
  <dbl> <dbl> <chr>      <chr>                         <dbl>
1  1977   544 Alaska     officer_state_total          397220
2  1977  7380 Alabama    officer_state_total         3782571
3  1977  3344 Arkansas   officer_state_total         2207195
4  1977  6414 Arizona    officer_state_total         2427296
5  1977 65596 California officer_state_total        22350332
6  1977  7337 Colorado   officer_state_total         2696179
# A tibble: 1,938 x 4
    YEAR VALUE STATE                VARIABLE           
   <dbl> <dbl> <chr>                <chr>              
 1  1977  137. Alaska               police_per_100k_lag
 2  1977  195. Alabama              police_per_100k_lag
 3  1977  152. Arkansas             police_per_100k_lag
 4  1977  264. Arizona              police_per_100k_lag
 5  1977  293. California           police_per_100k_lag
 6  1977  272. Colorado             police_per_100k_lag
 7  1977  196. Connecticut          police_per_100k_lag
 8  1977  697. District of Columbia police_per_100k_lag
 9  1977  171. Delaware             police_per_100k_lag
10  1977  277. Florida              police_per_100k_lag
# … with 1,928 more rows

Unemployment

The first thing we need to do with the unemployment data is combine the data across the different states. We can do that using the bind_rows() function of dplyr which appends the data together based on the presence of columns with the same name in the different tibbles. We will use the map_df() function of the purrr package to allow us to do this across each tibble in our list. We will then select just the annual data for each state and year and we will rename our variables to be consistent with some of other data that we are working with. Thus we would like our variables to be YEAR, VALUE and VARIABLE in all caps.

# A tibble: 6 x 15
  STATE   Year   Jan   Feb   Mar   Apr   May   Jun   Jul   Aug   Sep   Oct   Nov
  <chr>  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Alaba…  1977   7.5   9     7.7   7.2   6.8   8.6   8     7.8   6.7   6.3   6.3
2 Alaba…  1978   7.1   6.9   6.2   5.4   5.1   6.9   6.7   6.7   6.5   6.3   6.3
3 Alaba…  1979   6.7   7.5   6.9   6.6   6.4   8.4   7.7   7.8   7.1   7.2   6.9
4 Alaba…  1980   7.7   7.8   7.4   7.4   8.4   9.7  10.4  10.3   9.3   9.6   9.4
5 Alaba…  1981  10    10.3   9.5   9.1   9.4  11.1  10.4  10.9  10.8  11.7  11.5
6 Alaba…  1982  13.2  13.2  12.9  12.6  12.8  14.5  14.7  14.8  14.7  15.1  15.4
# … with 2 more variables: Dec <dbl>, Annual <dbl>
# A tibble: 6 x 4
  STATE    YEAR VALUE VARIABLE         
  <chr>   <dbl> <dbl> <chr>            
1 Alabama  1977   7.3 Unemployment_rate
2 Alabama  1978   6.4 Unemployment_rate
3 Alabama  1979   7.2 Unemployment_rate
4 Alabama  1980   8.9 Unemployment_rate
5 Alabama  1981  10.6 Unemployment_rate
6 Alabama  1982  14.1 Unemployment_rate

Poverty rate

Click here to see details about how the poverty data was wrangled

OK, now for wrangling the poverty data. First let’s take a look at it.

# A tibble: 6 x 6
  `NOTE: Number in thousa… ...2  ...3   ...4         ...5         ...6          
  <chr>                    <chr> <chr>  <chr>        <chr>        <chr>         
1 2018                     <NA>  <NA>    <NA>        <NA>          <NA>         
2 STATE                    Total Number "Standard\n… Percent      "Standard\ner…
3 Alabama                  4877  779    "65"         16           "1.3"         
4 Alaska                   720   94     "9"          13.1         "1.2"         
5 Arizona                  7241  929    "80"         12.80000000… "1.1000000000…
6 Arkansas                 2912  462    "38"         15.9         "1.3"

We can see that the column names are actually shifted down below the row with the year. So we will manually make these values the actual column names.

Let’s also remove the rows where the column names are listed, like row number 2.

# A tibble: 6 x 6
  STATE      Total Number Number_se Percent            Percent_se        
  <chr>      <chr> <chr>  <chr>     <chr>              <chr>             
1 2018       <NA>  <NA>   <NA>      <NA>               <NA>              
2 Alabama    4877  779    65        16                 1.3               
3 Alaska     720   94     9         13.1               1.2               
4 Arizona    7241  929    80        12.800000000000001 1.1000000000000001
5 Arkansas   2912  462    38        15.9               1.3               
6 California 39150 4664   184       11.9               0.5

We can also see that there are some extra notes at the end of our data. This is why it is a good idea to look at both the head and tail of your data.

# A tibble: 6 x 6
  STATE                             Total Number Number_se Percent Percent_se   
  <chr>                             <chr> <chr>  <chr>     <chr>   <chr>        
1 Wisconsin                         4724  403    57        8.5     1.1000000000…
2 Wyoming                           468   49     20        10.4    4            
3 Standard errors shown in this ta… <NA>  <NA>   <NA>      <NA>    <NA>         
4 For information on confidentiali… <NA>  <NA>   <NA>      <NA>    <NA>         
5 Footnotes are available at <www.… <NA>  <NA>   <NA>      <NA>    <NA>         
6 SOURCE: U.S. Bureau of the Censu… <NA>  <NA>   <NA>      <NA>    <NA>

We can see that the strings for the state for these rows are very long. We can also see that there are rows that just have the year, where the state is only 4 characters long. We will create a new variable called length_state based on the number of characters in the STATE values. We will use the str_length() function of the stringr package. We need to use the map_dbl() function to apply this to each row of the STATE variable. The map() function creates a list, whereas the map_dbl() function creates a vector of class double. If we were to use map() we would need to use unlist() and pull().

# A tibble: 2,136 x 7
   STATE         Total Number Number_se Percent      Percent_se     length_state
   <chr>         <chr> <chr>  <chr>     <chr>        <chr>                 <dbl>
 1 2018          <NA>  <NA>   <NA>      <NA>         <NA>                      4
 2 Alabama       4877  779    65        16           1.3                       7
 3 Alaska        720   94     9         13.1         1.2                       6
 4 Arizona       7241  929    80        12.80000000… 1.10000000000…            7
 5 Arkansas      2912  462    38        15.9         1.3                       8
 6 California    39150 4664   184       11.9         0.5                      10
 7 Colorado      5739  521    51        9.099999999… 0.90000000000…            8
 8 Connecticut   3413  348    43        10.19999999… 1.3                      11
 9 Delaware      976   72     9         7.400000000… 1                         8
10 District of … 692   102    8         14.69999999… 1.10000000000…           20
# … with 2,126 more rows

Great, now let’s look at the tail with our new variable length_state

[1]   9   7 285 164 129 101
# A tibble: 6 x 7
  STATE       Total Number Number_se Percent        Percent_se      length_state
  <chr>       <chr> <chr>  <chr>     <chr>          <chr>                  <dbl>
1 Vermont     520   62     22        12             4                          7
2 Virginia    5204  647    72        12.4           1.3                        8
3 Washington  4223  538    65        12.6999999999… 1.399999999999…           10
4 West Virgi… 1952  297    49        15.1999999999… 2.299999999999…           13
5 Wisconsin   4724  403    57        8.5            1.100000000000…            9
6 Wyoming     468   49     20        10.4           4                          7

Looks good!

Now let’s select all the states that are actually year values to create a new variable about the year. We can do so by using the str_detect() function of the stringr package to look for digits or values of 0-9. This is indicated by using the "[:digit:]".

As you can see in the RStudio cheatsheet about regular expressions this notation indicates any digit between 0 and 9.

# A tibble: 41 x 7
   STATE     Total Number Number_se Percent Percent_se length_state
   <chr>     <chr> <chr>  <chr>     <chr>   <chr>             <dbl>
 1 2018      <NA>  <NA>   <NA>      <NA>    <NA>                  4
 2 2017 (21) <NA>  <NA>   <NA>      <NA>    <NA>                  9
 3 2017      <NA>  <NA>   <NA>      <NA>    <NA>                  4
 4 2016      <NA>  <NA>   <NA>      <NA>    <NA>                  4
 5 2015      <NA>  <NA>   <NA>      <NA>    <NA>                  4
 6 2014      <NA>  <NA>   <NA>      <NA>    <NA>                  4
 7 2013 (19) <NA>  <NA>   <NA>      <NA>    <NA>                  9
 8 2013 (18) <NA>  <NA>   <NA>      <NA>    <NA>                  9
 9 2012      <NA>  <NA>   <NA>      <NA>    <NA>                  4
10 2011      <NA>  <NA>   <NA>      <NA>    <NA>                  4
11 2010 (17) <NA>  <NA>   <NA>      <NA>    <NA>                  9
12 2009      <NA>  <NA>   <NA>      <NA>    <NA>                  4
13 2008      <NA>  <NA>   <NA>      <NA>    <NA>                  4
14 2007      <NA>  <NA>   <NA>      <NA>    <NA>                  4
15 2006      <NA>  <NA>   <NA>      <NA>    <NA>                  4
16 2005      <NA>  <NA>   <NA>      <NA>    <NA>                  4
17 2004 (14) <NA>  <NA>   <NA>      <NA>    <NA>                  9
18 2003      <NA>  <NA>   <NA>      <NA>    <NA>                  4
19 2002      <NA>  <NA>   <NA>      <NA>    <NA>                  4
20 2001      <NA>  <NA>   <NA>      <NA>    <NA>                  4
21 2000 (12) <NA>  <NA>   <NA>      <NA>    <NA>                  9
22 1999 (11) <NA>  <NA>   <NA>      <NA>    <NA>                  9
23 1998      <NA>  <NA>   <NA>      <NA>    <NA>                  4
24 1997      <NA>  <NA>   <NA>      <NA>    <NA>                  4
25 1996      <NA>  <NA>   <NA>      <NA>    <NA>                  4
26 1995      <NA>  <NA>   <NA>      <NA>    <NA>                  4
27 1994      <NA>  <NA>   <NA>      <NA>    <NA>                  4
28 1993 (10) <NA>  <NA>   <NA>      <NA>    <NA>                  9
29 1992 (9)  <NA>  <NA>   <NA>      <NA>    <NA>                  8
30 1991 (8)  <NA>  <NA>   <NA>      <NA>    <NA>                  8
31 1990      <NA>  <NA>   <NA>      <NA>    <NA>                  4
32 1989      <NA>  <NA>   <NA>      <NA>    <NA>                  4
33 1988      <NA>  <NA>   <NA>      <NA>    <NA>                  4
34 1987 (7)  <NA>  <NA>   <NA>      <NA>    <NA>                  8
35 1986      <NA>  <NA>   <NA>      <NA>    <NA>                  4
36 1985      <NA>  <NA>   <NA>      <NA>    <NA>                  4
37 1984      <NA>  <NA>   <NA>      <NA>    <NA>                  4
38 1983 (6)  <NA>  <NA>   <NA>      <NA>    <NA>                  8
39 1982      <NA>  <NA>   <NA>      <NA>    <NA>                  4
40 1981 (5)  <NA>  <NA>   <NA>      <NA>    <NA>                  8
41 1980      <NA>  <NA>   <NA>      <NA>    <NA>                  4

Some of the years (2013 and 2017) are listed twice with a number in parentheses, others are just listed once with a number in parentheses. Looking at the technical documentation, this seems to do with updates to the definition of poverty and to the methods used to estimate poverty levels. See here and here for more information. We will simply select one of the sets of data for 2013 and 2017.

# A tibble: 0 x 7
# … with 7 variables: STATE <chr>, Total <chr>, Number <chr>, Number_se <chr>,
#   Percent <chr>, Percent_se <chr>, length_state <dbl>

First let’s add the year value to our data.

There should be consistently data for 51 states (including DC). We can see that sometimes DC is spelled out and sometimes it is not.

# A tibble: 52 x 1
   STATE               
   <chr>               
 1 Alabama             
 2 Alaska              
 3 Arizona             
 4 Arkansas            
 5 California          
 6 Colorado            
 7 Connecticut         
 8 Delaware            
 9 District of Columbia
10 Florida             
11 Georgia             
12 Hawaii              
13 Idaho               
14 Illinois            
15 Indiana             
16 Iowa                
17 Kansas              
18 Kentucky            
19 Louisiana           
20 Maine               
21 Maryland            
22 Massachusetts       
23 Michigan            
24 Minnesota           
25 Mississippi         
26 Missouri            
27 Montana             
28 Nebraska            
29 Nevada              
30 New Hampshire       
31 New Jersey          
32 New Mexico          
33 New York            
34 North Carolina      
35 North Dakota        
36 Ohio                
37 Oklahoma            
38 Oregon              
39 Pennsylvania        
40 Rhode Island        
41 South Carolina      
42 South Dakota        
43 Tennessee           
44 Texas               
45 Utah                
46 Vermont             
47 Virginia            
48 Washington          
49 West Virginia       
50 Wisconsin           
51 Wyoming             
52 D.C.

Now we will replace "D.C." with "District of Columbia" using str_replace(). We can use the tally() function of the dplyr package to check that we have fewer now.

# A tibble: 1 x 1
      n
  <int>
1    51

Great! Now are each of the states occurring as often as the unique year values? We can first check how many year values there are. Then can use the count() function of the dplyr package to check how often the states are repeated.

# A tibble: 1 x 1
      n
  <int>
1    41

There are 41 different sets of data according to year values.

# A tibble: 51 x 2
   STATE                    n
   <chr>                <int>
 1 Alabama                 41
 2 Alaska                  41
 3 Arizona                 41
 4 Arkansas                41
 5 California              41
 6 Colorado                41
 7 Connecticut             41
 8 Delaware                41
 9 District of Columbia    41
10 Florida                 41
11 Georgia                 41
12 Hawaii                  41
13 Idaho                   41
14 Illinois                41
15 Indiana                 41
16 Iowa                    41
17 Kansas                  41
18 Kentucky                41
19 Louisiana               41
20 Maine                   41
21 Maryland                41
22 Massachusetts           41
23 Michigan                41
24 Minnesota               41
25 Mississippi             41
26 Missouri                41
27 Montana                 41
28 Nebraska                41
29 Nevada                  41
30 New Hampshire           41
31 New Jersey              41
32 New Mexico              41
33 New York                41
34 North Carolina          41
35 North Dakota            41
36 Ohio                    41
37 Oklahoma                41
38 Oregon                  41
39 Pennsylvania            41
40 Rhode Island            41
41 South Carolina          41
42 South Dakota            41
43 Tennessee               41
44 Texas                   41
45 Utah                    41
46 Vermont                 41
47 Virginia                41
48 Washington              41
49 West Virginia           41
50 Wisconsin               41
51 Wyoming                 41

Indeed, looks like each of the states are repeated the same number of times!

Now let’s create a new variable YEAR that repeats the year values for all of the different states and for the row that has just the year value for a total of 52.

[1] TRUE

Now we will add this to our poverty_rate_data. We will also remove the length_state variable using the select() function of the dplyr package and a minus sign before the variable name.

# A tibble: 2,132 x 7
    STATE         Total Number Number_se Percent       Percent_se     year_value
    <chr>         <chr> <chr>  <chr>     <chr>         <chr>          <chr>     
  1 2018          <NA>  <NA>   <NA>      <NA>          <NA>           2018      
  2 Alabama       4877  779    65        16            1.3            2018      
  3 Alaska        720   94     9         13.1          1.2            2018      
  4 Arizona       7241  929    80        12.800000000… 1.10000000000… 2018      
  5 Arkansas      2912  462    38        15.9          1.3            2018      
  6 California    39150 4664   184       11.9          0.5            2018      
  7 Colorado      5739  521    51        9.0999999999… 0.90000000000… 2018      
  8 Connecticut   3413  348    43        10.199999999… 1.3            2018      
  9 Delaware      976   72     9         7.4000000000… 1              2018      
 10 District of … 692   102    8         14.699999999… 1.10000000000… 2018      
 11 Florida       21117 2883   173       13.699999999… 0.80000000000… 2018      
 12 Georgia       10423 1548   115       14.800000000… 1.10000000000… 2018      
 13 Hawaii        1393  128    16        9.1999999999… 1.10000000000… 2018      
 14 Idaho         1766  202    25        11.5          1.39999999999… 2018      
 15 Illinois      12589 1292   130       10.300000000… 1              2018      
 16 Indiana       6582  761    68        11.6          1              2018      
 17 Iowa          3110  277    30        8.9000000000… 1              2018      
 18 Kansas        2835  212    27        7.5           0.90000000000… 2018      
 19 Kentucky      4445  696    76        15.699999999… 1.7            2018      
 20 Louisiana     4510  858    38        19            0.80000000000… 2018      
 21 Maine         1321  153    19        11.6          1.39999999999… 2018      
 22 Maryland      6030  480    50        8             0.80000000000… 2018      
 23 Massachusetts 6883  601    59        8.6999999999… 0.90000000000… 2018      
 24 Michigan      9913  1036   78        10.5          0.80000000000… 2018      
 25 Minnesota     5746  456    47        7.9000000000… 0.80000000000… 2018      
 26 Mississippi   2899  567    27        19.600000000… 0.90000000000… 2018      
 27 Missouri      6026  745    81        12.4          1.3            2018      
 28 Montana       1041  107    12        10.300000000… 1.10000000000… 2018      
 29 Nebraska      1893  199    25        10.5          1.3            2018      
 30 Nevada        3008  390    37        13            1.2            2018      
 31 New Hampshire 1349  82     11        6.0999999999… 0.80000000000… 2018      
 32 New Jersey    8790  725    65        8.1999999999… 0.69999999999… 2018      
 33 New Mexico    2054  342    27        16.600000000… 1.3            2018      
 34 New York      19343 2145   110       11.1          0.59999999999… 2018      
 35 North Caroli… 10369 1355   96        13.1          0.90000000000… 2018      
 36 North Dakota  745   72     9         9.6999999999… 1.2            2018      
 37 Ohio          11452 1365   115       11.9          1              2018      
 38 Oklahoma      3858  518    48        13.4          1.2            2018      
 39 Oregon        4172  404    45        9.6999999999… 1.10000000000… 2018      
 40 Pennsylvania  12519 1476   124       11.800000000… 1              2018      
 41 Rhode Island  1036  92     15        8.9000000000… 1.39999999999… 2018      
 42 South Caroli… 5036  642    45        12.800000000… 0.90000000000… 2018      
 43 South Dakota  853   90     8         10.6          0.90000000000… 2018      
 44 Tennessee     6665  800    73        12            1.10000000000… 2018      
 45 Texas         28497 3894   173       13.699999999… 0.59999999999… 2018      
 46 Utah          3173  219    37        6.9000000000… 1.2            2018      
 47 Vermont       616   60     7         9.6999999999… 1.10000000000… 2018      
 48 Virginia      8393  821    86        9.8000000000… 1              2018      
 49 Washington    7555  647    101       8.5999999999… 1.3            2018      
 50 West Virginia 1762  279    21        15.9          1.2            2018      
 51 Wisconsin     5795  499    54        8.5999999999… 0.90000000000… 2018      
 52 Wyoming       565   53     6         9.5           1.10000000000… 2018      
 53 2017 (21)     <NA>  <NA>   <NA>      <NA>          <NA>           2017 (21) 
 54 Alabama       4801  735    58        15.300000000… 1.2            2017 (21) 
 55 Alaska        719   87     12        12.1          1.7            2017 (21) 
 56 Arizona       6981  951    104       13.6          1.5            2017 (21) 
 57 Arkansas      2921  436    33        14.9          1.10000000000… 2017 (21) 
 58 California    39247 4759   182       12.1          0.5            2017 (21) 
 59 Colorado      5527  489    51        8.9000000000… 0.90000000000… 2017 (21) 
 60 Connecticut   3553  377    43        10.6          1.2            2017 (21) 
 61 Delaware      967   85     10        8.8000000000… 1.10000000000… 2017 (21) 
 62 District of … 691   96     8         13.9          1.10000000000… 2017 (21) 
 63 Florida       20909 2809   173       13.4          0.80000000000… 2017 (21) 
 64 Georgia       10231 1339   104       13.1          1              2017 (21) 
 65 Hawaii        1402  149    17        10.6          1.2            2017 (21) 
 66 Idaho         1730  199    23        11.5          1.3            2017 (21) 
 67 Illinois      12597 1454   102       11.5          0.80000000000… 2017 (21) 
 68 Indiana       6532  762    70        11.699999999… 1.10000000000… 2017 (21) 
 69 Iowa          3053  230    33        7.5           1.10000000000… 2017 (21) 
 70 Kansas        2868  411    33        14.300000000… 1.2            2017 (21) 
 71 Kentucky      4395  591    76        13.5          1.7            2017 (21) 
 72 Louisiana     4535  931    48        20.5          1.10000000000… 2017 (21) 
 73 Maine         1315  163    19        12.4          1.39999999999… 2017 (21) 
 74 Maryland      5977  451    58        7.5999999999… 1              2017 (21) 
 75 Massachusetts 6784  763    71        11.199999999… 1              2017 (21) 
 76 Michigan      9895  1135   88        11.5          0.90000000000… 2017 (21) 
 77 Minnesota     5619  479    57        8.5           1              2017 (21) 
 78 Mississippi   2948  544    27        18.5          0.90000000000… 2017 (21) 
 79 Missouri      5988  683    81        11.4          1.39999999999… 2017 (21) 
 80 Montana       1041  107    11        10.300000000… 1              2017 (21) 
 81 Nebraska      1878  216    25        11.5          1.3            2017 (21) 
 82 Nevada        2979  392    37        13.199999999… 1.2            2017 (21) 
 83 New Hampshire 1333  95     13        7.2000000000… 1              2017 (21) 
 84 New Jersey    9015  894    95        9.9000000000… 1.10000000000… 2017 (21) 
 85 New Mexico    2035  402    28        19.699999999… 1.39999999999… 2017 (21) 
 86 New York      19735 2510   141       12.699999999… 0.69999999999… 2017 (21) 
 87 North Caroli… 10297 1567   96        15.199999999… 0.90000000000… 2017 (21) 
 88 North Dakota  740   92     11        12.4          1.5            2017 (21) 
 89 Ohio          11491 1479   98        12.9          0.90000000000… 2017 (21) 
 90 Oklahoma      3817  490    47        12.800000000… 1.2            2017 (21) 
 91 Oregon        4202  482    55        11.5          1.3            2017 (21) 
 92 Pennsylvania  12568 1373   94        10.9          0.80000000000… 2017 (21) 
 93 Rhode Island  1046  118    15        11.300000000… 1.39999999999… 2017 (21) 
 94 South Caroli… 4955  756    55        15.199999999… 1.10000000000… 2017 (21) 
 95 South Dakota  870   93     15        10.699999999… 1.7            2017 (21) 
 96 Tennessee     6699  759    65        11.300000000… 1              2017 (21) 
 97 Texas         28090 3715   186       13.199999999… 0.69999999999… 2017 (21) 
 98 Utah          3130  272    34        8.6999999999… 1.10000000000… 2017 (21) 
 99 Vermont       613   53     6         8.5999999999… 1              2017 (21) 
100 Virginia      8242  862    71        10.5          0.90000000000… 2017 (21) 
# … with 2,032 more rows

Looks good! Now we will remove the rows that have just the year values by only preserving those with alpha characters.

Now let’s remove the older data for 2013 and 2017 which is the data that appears lower in the tibble.

We also want to just keep the first 4 digits of the year_value and create a YEAR variable. We need to pull the year_value data because str_sub() expects a character vector not a tibble.

# A tibble: 1,989 x 8
   STATE       Total Number Number_se Percent     Percent_se    year_value YEAR 
   <chr>       <chr> <chr>  <chr>     <chr>       <chr>         <chr>      <chr>
 1 Alabama     4877  779    65        16          1.3           2018       2018 
 2 Alaska      720   94     9         13.1        1.2           2018       2018 
 3 Arizona     7241  929    80        12.8000000… 1.1000000000… 2018       2018 
 4 Arkansas    2912  462    38        15.9        1.3           2018       2018 
 5 California  39150 4664   184       11.9        0.5           2018       2018 
 6 Colorado    5739  521    51        9.09999999… 0.9000000000… 2018       2018 
 7 Connecticut 3413  348    43        10.1999999… 1.3           2018       2018 
 8 Delaware    976   72     9         7.40000000… 1             2018       2018 
 9 District o… 692   102    8         14.6999999… 1.1000000000… 2018       2018 
10 Florida     21117 2883   173       13.6999999… 0.8000000000… 2018       2018 
# … with 1,979 more rows

Looks good! Now we will just remove the extra variables and rename the variables we want to keep to be similar to our other data.

# A tibble: 6 x 4
  STATE      VALUE  YEAR VARIABLE    
  <chr>      <dbl> <dbl> <chr>       
1 Alabama     16    2018 Poverty_rate
2 Alaska      13.1  2018 Poverty_rate
3 Arizona     12.8  2018 Poverty_rate
4 Arkansas    15.9  2018 Poverty_rate
5 California  11.9  2018 Poverty_rate
6 Colorado     9.1  2018 Poverty_rate

Looks great!

Violent crime

Click here to see details about how the violent crime data was wrangled

The crime_data was imported using read_lines() and we have some lines that we don’t necessarily need. A large amount of the original data is notes at the end of the table. We want to remove these lines. We can determine where they start by searching for the row that contains the first statement of these notes using the str_which() function of the stringr package. We will subtract one from this as there is a blank line in between.

[1] ",,\"Vermont - The state UCR Program was unable to provide complete 1997 offense figures in accordance with UCR guidelines.  The 1996 and 1997 percent changes within the geographic division in which Vermont is categorized were applied to the valid 1996 state total to effect the 1997 state total.\""                                                                                                              
[2] "  "                                                                                                                                                                                                                                                                                                                                                                                                                     
[3] " "                                                                                                                                                                                                                                                                                                                                                                                                                      
[4] ",,\"Wisconsin - The state UCR Program was unable to provide complete 1998 offense figures in accordance with UCR guidelines.  The state total was estimated by using 1997 figures for the nonreporting areas and applying 1997 versus 1998 percentage changes for the division in which Wisconsin is located.  The estimates for the nonreporting areas were then increased by any actual 1998 crime counts received.\""
[5] " "                                                                                                                                                                                                                                                                                                                                                                                                                      
[6] "\"Sources: FBI, Uniform Crime Reports, prepared by the National Archive of Criminal Justice Data\" "                                                                                                                                                                                                                                                                                                                    
[1] "2009,     544270,       1196,          11,         172,,          78,         935 "            
[2] "2010,     564554,       1117,           8,         162,,          77,         870 "            
[3] "2011,     567356,       1245,          18,         146,,          71,       1010 "             
[4] "2012,     576626,       1161,          14,         154,,          61,         932 "            
[5] "2013,     583223,       1212,          17,         144,         204,          74,         917 "
[6] "2014,     584153,       1142,          16,         126,         174,          53,         899 "

There are lines for each year from 1977 to 2014 as well as four lines about each state and the header information for each state. Here you can see what the original data looks like:

We want to delete the header information and only retain the lines numeric values or state names. Thus since there are 38 years worth of data for each state and 4 lines for each header, then each state has 42 lines. We want to delete the lines between and including line 2 to 4 for each state. We will save the header information once to use later.

[1] "Estimated crime in Alabama"                                                                                                           
[2] "\n,,National or state crime,,,,,,,"                                                                                                   
[3] "\n,,Violent crime,,,,,,,"                                                                                                             
[4] "\nYear,Population,Violent crime total,Murder and nonnegligent Manslaughter,Legacy rape /1,Revised rape /2,Robbery,Aggravated assault,"
[5] "1977,   3690000,      15293,         524,         929,,       3572,      10268 "                                                      
[6] "1978,   3742000,      15682,         499,         954,,       3708,      10521 "

So starting at line 2 and and 3 and 4 we create a sequence of numbers that increase by the number of rows of the length of the individual state data. We can do so using the base seq() function. We can take a look at these in order using the base sort() function.

  [1]    2    3    4   44   45   46   86   87   88  128  129  130  170  171  172
 [16]  212  213  214  254  255  256  296  297  298  338  339  340  380  381  382
 [31]  422  423  424  464  465  466  506  507  508  548  549  550  590  591  592
 [46]  632  633  634  674  675  676  716  717  718  758  759  760  800  801  802
 [61]  842  843  844  884  885  886  926  927  928  968  969  970 1010 1011 1012
 [76] 1052 1053 1054 1094 1095 1096 1136 1137 1138 1178 1179 1180 1220 1221 1222
 [91] 1262 1263 1264 1304 1305 1306 1346 1347 1348 1388 1389 1390 1430 1431 1432
[106] 1472 1473 1474 1514 1515 1516 1556 1557 1558 1598 1599 1600 1640 1641 1642
[121] 1682 1683 1684 1724 1725 1726 1766 1767 1768 1808 1809 1810 1850 1851 1852
[136] 1892 1893 1894 1934 1935 1936 1976 1977 1978 2018 2019 2020 2060 2061 2062
[151] 2102 2103 2104

Thus we will delete lines 2, 3, and 4 and then skip 40 lines (to account for the state information for the first state, the lines of information for the 38 years, and then the state information for the next state) and then delete the next 3 consecutive lines and so on. We can indeed see that line 44-46 are what we wish to remove.

[1] "\n,,National or state crime,,,,,,,"                                                                                                   
[2] "\n,,Violent crime,,,,,,,"                                                                                                             
[3] "\nYear,Population,Violent crime total,Murder and nonnegligent Manslaughter,Legacy rape /1,Revised rape /2,Robbery,Aggravated assault,"

Nice!

Now we can select all the lines that have state information. We can repeat each of these for the 38 years for each state as well as this line that contains the state information by using the base rep() function with the each = argument. Finally we will remove the "Estimated crime in " portion of the string using the str_remove() function of the stringr package. We will later combine this with the crime data.

 [1] "Estimated crime in Alabama"             
 [2] "Estimated crime in Alaska"              
 [3] "Estimated crime in Arizona"             
 [4] "Estimated crime in Arkansas"            
 [5] "Estimated crime in California"          
 [6] "Estimated crime in Colorado"            
 [7] "Estimated crime in Connecticut"         
 [8] "Estimated crime in Delaware"            
 [9] "Estimated crime in District of Columbia"
[10] "Estimated crime in Florida"             
[11] "Estimated crime in Georgia"             
[12] "Estimated crime in Hawaii"              
[13] "Estimated crime in Idaho"               
[14] "Estimated crime in Illinois"            
[15] "Estimated crime in Indiana"             
[16] "Estimated crime in Iowa"                
[17] "Estimated crime in Kansas"              
[18] "Estimated crime in Kentucky"            
[19] "Estimated crime in Louisiana"           
[20] "Estimated crime in Maine"               
[21] "Estimated crime in Maryland"            
[22] "Estimated crime in Massachusetts"       
[23] "Estimated crime in Michigan"            
[24] "Estimated crime in Minnesota"           
[25] "Estimated crime in Mississippi"         
[26] "Estimated crime in Missouri"            
[27] "Estimated crime in Montana"             
[28] "Estimated crime in Nebraska"            
[29] "Estimated crime in Nevada"              
[30] "Estimated crime in New Hampshire"       
[31] "Estimated crime in New Jersey"          
[32] "Estimated crime in New Mexico"          
[33] "Estimated crime in New York"            
[34] "Estimated crime in North Carolina"      
[35] "Estimated crime in North Dakota"        
[36] "Estimated crime in Ohio"                
[37] "Estimated crime in Oklahoma"            
[38] "Estimated crime in Oregon"              
[39] "Estimated crime in Pennsylvania"        
[40] "Estimated crime in Rhode Island"        
[41] "Estimated crime in South Carolina"      
[42] "Estimated crime in South Dakota"        
[43] "Estimated crime in Tennessee"           
[44] "Estimated crime in Texas"               
[45] "Estimated crime in Utah"                
[46] "Estimated crime in Vermont"             
[47] "Estimated crime in Virginia"            
[48] "Estimated crime in Washington"          
[49] "Estimated crime in West Virginia"       
[50] "Estimated crime in Wisconsin"           
[51] "Estimated crime in Wyoming"             
[1] "Alabama" "Alabama" "Alabama" "Alabama" "Alabama" "Alabama"

Nice! Now for the rest of the data. We now need to remove the lines with the state information.

[1] "1977,   3690000,      15293,         524,         929,,       3572,      10268 "
[2] "1978,   3742000,      15682,         499,         954,,       3708,      10521 "
[3] "1979,   3769000,      15578,         496,       1037,,       4127,       9918 " 
[4] "1980,   3861466,      17320,         509,       1158,,       5102,      10551 " 
[5] "1981,   3916000,      18423,         465,       1021,,       4952,      11985 " 
[6] "1982,   3943000,      17653,         417,       1026,,       4417,      11793 " 
[1] "2009,     544270,       1196,          11,         172,,          78,         935 "            
[2] "2010,     564554,       1117,           8,         162,,          77,         870 "            
[3] "2011,     567356,       1245,          18,         146,,          71,       1010 "             
[4] "2012,     576626,       1161,          14,         154,,          61,         932 "            
[5] "2013,     583223,       1212,          17,         144,         204,          74,         917 "
[6] "2014,     584153,       1142,          16,         126,         174,          53,         899 "

It appears that the data is comma separated with 8 columns. One of the middle columns often has no values, we need to fill these in with NAs. We can use the read_csv() function from the readr package to do this. It turns out you don’t have to have a file, but you can also use a string our a vector.

[1] "1977,   3690000,      15293,         524,         929,,       3572,      10268 "
[2] "1978,   3742000,      15682,         499,         954,,       3708,      10521 "
[3] "1979,   3769000,      15578,         496,       1037,,       4127,       9918 " 
[4] "1980,   3861466,      17320,         509,       1158,,       5102,      10551 " 
[5] "1981,   3916000,      18423,         465,       1021,,       4952,      11985 " 
[6] "1982,   3943000,      17653,         417,       1026,,       4417,      11793 "

Nice! Now we just need our column names. Recall that we saved this information.

[1] "\nYear,Population,Violent crime total,Murder and nonnegligent Manslaughter,Legacy rape /1,Revised rape /2,Robbery,Aggravated assault,"
# A tibble: 6 x 8
   Year Population Violent_crime_t… Murder_and_nonn… Legacy_rape Revised_rape
  <dbl>      <dbl>            <dbl>            <dbl>       <dbl>        <dbl>
1  1977    3690000            15293              524         929           NA
2  1978    3742000            15682              499         954           NA
3  1979    3769000            15578              496        1037           NA
4  1980    3861466            17320              509        1158           NA
5  1981    3916000            18423              465        1021           NA
6  1982    3943000            17653              417        1026           NA
# … with 2 more variables: Robbery <dbl>, Aggravated_assault <dbl>

We also want to combine this with the state information we collected earlier. We will use the bind_cols() function of the dplyr package to do this. This requires that the data have the same number of rows.

Now we will rename the Viol_crime_count variable to be Variable and we will remove all of the other columns except for Year. We will also rename the variables to look like the other datasets.

# A tibble: 1,938 x 4
    YEAR STATE   VARIABLE         VALUE
   <dbl> <chr>   <chr>            <dbl>
 1  1977 Alabama Viol_crime_count 15293
 2  1978 Alabama Viol_crime_count 15682
 3  1979 Alabama Viol_crime_count 15578
 4  1980 Alabama Viol_crime_count 17320
 5  1981 Alabama Viol_crime_count 18423
 6  1982 Alabama Viol_crime_count 17653
 7  1983 Alabama Viol_crime_count 16471
 8  1984 Alabama Viol_crime_count 17204
 9  1985 Alabama Viol_crime_count 18398
10  1986 Alabama Viol_crime_count 22616
# … with 1,928 more rows

from Michael:

RTC laws

Click here to see details about how the RTC Law data was wrangled

The information about the laws for each state are located on page 62 of the Donohue, et al. article, so first we will select just this page. We can print part of the character string for this page using the utils str() function and the ncar.max argument.

 chr "                                         Table A1: RTC Adoption Dates\n         State       Effective Date of RTC Law Fraction of Year In Effect Year of Passage RTC Date (Synthetic Controls Analysis)\n      Alabama                    1975                                                                    1975\n       Alaska                 10/1/1994                          0.252                                   1995\n       Arizona                7/17/1994                          0.460                                   1995\n      Arkansas                7/27/1995                          0.433                                   1996\n     California                  N/A                                                                        0\n      Colorado                5/17/2003                          0.627                                   2003\n    Connecticut                  1970                                                                    1970\n     "| __truncated__

We can also use the cat function to see the data printed nicely to see what we are going for.

                                         Table A1: RTC Adoption Dates
         State       Effective Date of RTC Law Fraction of Year In Effect Year of Passage RTC Date (Synthetic Controls Analysis)
      Alabama                    1975                                                                    1975
       Alaska                 10/1/1994                          0.252                                   1995
       Arizona                7/17/1994                          0.460                                   1995
      Arkansas                7/27/1995                          0.433                                   1996
     California                  N/A                                                                        0
      Colorado                5/17/2003                          0.627                                   2003
    Connecticut                  1970                                                                    1970
      Delaware                   N/A                                                                        0
District of Columbia             N/A                                                                        0
       Florida                10/1/1987                          0.252                                   1988
       Georgia                8/25/1989                          0.353                                   1990
       Hawaii                    N/A                                                                        0
        Idaho                  7/1/1990                          0.504                                   1990
       Illinois                1/5/2014                                                                  2014
       Indiana                1/15/1980                          0.962                                   1980
         Iowa                  1/1/2011                          1.000                                   2011
       Kansas                  1/1/2007                          1.000                                   2007
      Kentucky                10/1/1996                          0.251                                   1997
      Louisiana               4/19/1996                          0.702                                   1996
        Maine                 9/19/1985                          0.285                                   1986
      Maryland                   N/A                                                                        0
   Massachusetts                 N/A                                                                        0
      Michigan                 7/1/2001                          0.504                                   2001
     Minnesota                5/28/2003                          0.597                                   2003
     Mississippi               7/1/1990                          0.504                                   1990
      Missouri                2/26/2004                          0.847                                   2004
      Montana                 10/1/1991                          0.252                                   1992
      Nebraska                 1/1/2007                          1.000                                   2007
       Nevada                 10/1/1995                          0.252                                   1996
  New Hampshire                  1959                                                                    1959
     New Jersey                  N/A                                                                        0
    New Mexico                 1/1/2004                          1.000                                   2004
     New York                    N/A                                                                        0
  North Carolina              12/1/1995                          0.085                                   1996
   North Dakota                8/1/1985                          0.419                                   1986
         Ohio                  4/8/2004                          0.732                                   2004
     Oklahoma                  1/1/1996                          1.000                                   1996
       Oregon                  1/1/1990                          1.000                                   1990
   Pennsylvania               6/17/1989                          0.542                                   1989
    Philadelphia             10/11/1995                          0.225                                   1996
   Rhode Island                  N/A                                                                        0
  South Carolina              8/23/1996                          0.358                                   1997
   South Dakota                7/1/1985                          0.504                                   1985
     Tennessee                10/1/1996                          0.251                                   1997
        Texas                  1/1/1996                          1.000                                   1996
         Utah                  5/1/1995                          0.671                                   1995
      Vermont                    1970                                                                    1970
       Virginia                5/5/1995                          0.660                                   1995
    Washington                   1961                                                                    1961
   West Virginia               7/7/1989                          0.488                                   1990
     Wisconsin                11/1/2011                          0.167                                   2012
      Wyoming                 10/1/1994                          0.252                                   1995
                                                             60

We can see that this is one continuous character string. We can separate into lines based on the presence of "\n" in the string using the str_split() function of the stringr package. We need to unlist the data first, as the output of str_split() is a list. Finally, we can convert it to a tibble using the as.tibble() function of the tibble package. We also see that we don’t need the first line about the table. We can remove this with the slice() function of the dplyr package. We can also use this to remove the column names so that we can replace them. Thus we will use slice(-(1:2)) to remove the first two lines.

So we will split and unlit() the data.

# A tibble: 6 x 1
  value                                                                         
  <chr>                                                                         
1 "      Alabama                    1975                                       …
2 "       Alaska                 10/1/1994                          0.252      …
3 "       Arizona                7/17/1994                          0.460      …
4 "      Arkansas                7/27/1995                          0.433      …
5 "     California                  N/A                                        …
6 "      Colorado                5/17/2003                          0.627      …
# A tibble: 6 x 1
  value                                                                         
  <chr>                                                                         
1 "    Washington                   1961                                       …
2 "   West Virginia               7/7/1989                          0.488      …
3 "     Wisconsin                11/1/2011                          0.167      …
4 "      Wyoming                 10/1/1994                          0.252      …
5 "                                                             60"             
6 ""

We also see by looking at the tail that we want to remove the last two lines. One is empty and the other has only 63 characters, which is the line with the page number.

# A tibble: 6 x 1
  RTC  
  <chr>
1 109  
2 109  
3 109  
4 109  
5 63   
6 0    
# A tibble: 1 x 1
  RTC                                                              
  <chr>                                                            
1 "                                                             60"
# A tibble: 1 x 1
  RTC  
  <chr>
1 ""

Now we will try splitting by spaces. We can show the output withe the first() and nth() functions of the dplyr package.

[[1]]
 [1] ""        ""        ""        ""        ""        ""        "Alabama"
 [8] ""        ""        ""        ""        ""        ""        ""       
[15] ""        ""        ""        ""        ""        ""        ""       
[22] ""        ""        ""        ""        ""        "1975"    ""       
[29] ""        ""        ""        ""        ""        ""        ""       
[36] ""        ""        ""        ""        ""        ""        ""       
[43] ""        ""        ""        ""        ""        ""        ""       
[50] ""        ""        ""        ""        ""        ""        ""       
[57] ""        ""        ""        ""        ""        ""        ""       
[64] ""        ""        ""        ""        ""        ""        ""       
[71] ""        ""        ""        ""        ""        ""        ""       
[78] ""        ""        ""        ""        ""        ""        ""       
[85] ""        ""        ""        ""        ""        ""        ""       
[92] ""        ""        ""        "1975"   
[[1]]
 [1] ""           ""           ""           ""           ""          
 [6] "California" ""           ""           ""           ""          
[11] ""           ""           ""           ""           ""          
[16] ""           ""           ""           ""           ""          
[21] ""           ""           ""           "N/A"        ""          
[26] ""           ""           ""           ""           ""          
[31] ""           ""           ""           ""           ""          
[36] ""           ""           ""           ""           ""          
[41] ""           ""           ""           ""           ""          
[46] ""           ""           ""           ""           ""          
[51] ""           ""           ""           ""           ""          
[56] ""           ""           ""           ""           ""          
[61] ""           ""           ""           ""           ""          
[66] ""           ""           ""           ""           ""          
[71] ""           ""           ""           ""           ""          
[76] ""           ""           ""           ""           ""          
[81] ""           ""           ""           ""           ""          
[86] ""           ""           ""           ""           ""          
[91] ""           ""           ""           ""           ""          
[96] "0"

Interesting, we can see that there are lots of spaces between the elements of the table and that they vary by line. For example there are 6 spaces before Alabama and 7 spaces before Alaska.

Overall, that didn’t work quite like we expected.

Recall from the cheatsheet that "\\s" indicates a space. There are also ways to specify how many spaces using curly brackets{}.

The spacing appears to vary quite a bit. WE can use the str_count() function of the stringr package to see how often we have white spaces larger than 5, 10, 15, or 40 spaces.

 [1] 3 4 4 4 3 4 2 3 2 4 4 3 4 3 4 4 4 4 4 4 3 2 4 4 4 4 4 4 4 2 3 3 3 3 3 4 4 4
[39] 3 3 2 3 3 4 4 4 3 4 2 3 4 4
 [1] 2 3 3 3 2 3 2 2 2 3 3 2 3 2 3 3 3 3 3 3 2 2 3 3 3 3 3 3 3 2 2 3 2 3 3 3 3 3
[39] 3 3 2 3 3 3 3 3 2 3 2 3 3 3
 [1] 2 3 3 3 2 3 2 2 1 3 3 2 3 2 3 3 3 3 3 3 2 2 3 3 3 3 3 3 3 2 2 3 2 2 3 3 3 3
[39] 3 2 2 2 3 3 3 3 2 3 2 3 3 3
 [1] 1 0 0 0 1 0 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0
[39] 0 0 1 0 0 0 0 0 1 0 1 0 0 0

Rows with white spaces with more than 40 consecutive spaces is less common. It appears to be the case in the 1st and 5th row.

If we take a look at those rows we can see that this occurs when we have a missing value.

                                         Table A1: RTC Adoption Dates
         State       Effective Date of RTC Law Fraction of Year In Effect Year of Passage RTC Date (Synthetic Controls Analysis)
      Alabama                    1975                                                                    1975
       Alaska                 10/1/1994                          0.252                                   1995
       Arizona                7/17/1994                          0.460                                   1995
      Arkansas                7/27/1995                          0.433                                   1996
     California                  N/A                                                                        0
      Colorado                5/17/2003                          0.627                                   2003
    Connecticut                  1970                                                                    1970
      Delaware                   N/A                                                                        0
District of Columbia             N/A                                                                        0
       Florida                10/1/1987                          0.252                                   1988
       Georgia                8/25/1989                          0.353                                   1990
       Hawaii                    N/A                                                                        0
        Idaho                  7/1/1990                          0.504                                   1990
       Illinois                1/5/2014                                                                  2014
       Indiana                1/15/1980                          0.962                                   1980
         Iowa                  1/1/2011                          1.000                                   2011
       Kansas                  1/1/2007                          1.000                                   2007
      Kentucky                10/1/1996                          0.251                                   1997
      Louisiana               4/19/1996                          0.702                                   1996
        Maine                 9/19/1985                          0.285                                   1986
      Maryland                   N/A                                                                        0
   Massachusetts                 N/A                                                                        0
      Michigan                 7/1/2001                          0.504                                   2001
     Minnesota                5/28/2003                          0.597                                   2003
     Mississippi               7/1/1990                          0.504                                   1990
      Missouri                2/26/2004                          0.847                                   2004
      Montana                 10/1/1991                          0.252                                   1992
      Nebraska                 1/1/2007                          1.000                                   2007
       Nevada                 10/1/1995                          0.252                                   1996
  New Hampshire                  1959                                                                    1959
     New Jersey                  N/A                                                                        0
    New Mexico                 1/1/2004                          1.000                                   2004
     New York                    N/A                                                                        0
  North Carolina              12/1/1995                          0.085                                   1996
   North Dakota                8/1/1985                          0.419                                   1986
         Ohio                  4/8/2004                          0.732                                   2004
     Oklahoma                  1/1/1996                          1.000                                   1996
       Oregon                  1/1/1990                          1.000                                   1990
   Pennsylvania               6/17/1989                          0.542                                   1989
    Philadelphia             10/11/1995                          0.225                                   1996
   Rhode Island                  N/A                                                                        0
  South Carolina              8/23/1996                          0.358                                   1997
   South Dakota                7/1/1985                          0.504                                   1985
     Tennessee                10/1/1996                          0.251                                   1997
        Texas                  1/1/1996                          1.000                                   1996
         Utah                  5/1/1995                          0.671                                   1995
      Vermont                    1970                                                                    1970
       Virginia                5/5/1995                          0.660                                   1995
    Washington                   1961                                                                    1961
   West Virginia               7/7/1989                          0.488                                   1990
     Wisconsin                11/1/2011                          0.167                                   2012
      Wyoming                 10/1/1994                          0.252                                   1995
                                                             60

So we will replace white spaces with more than 40 consecutive spaces with NA. Let’s also remove the leading white spaces that varies in front of the state names, as DC does not have any and this could cause a problem later. We will also replace any white spaces of 2 consecutive spaces or more , but less than 15 white spaces with “|” so that we can split the data based on this symbol. Thus we will also put these around the NA value that we are using replace the white spaces made of 40+ spaces.

# A tibble: 6 x 1
  RTC                              
  <chr>                            
1 Alabama||1975|N/A|1975           
2 Alaska||10/1/1994||0.252|||1995  
3 Arizona| 7/17/1994||0.460|||1995 
4 Arkansas| 7/27/1995||0.433|||1996
5 California||N/A|N/A|0            
6 Colorado| 5/17/2003||0.627|||2003

Now anytime there is one or more "|" we should have a column break. So now we will split the data by this symbol.

[[1]]
[1] "Alabama" "1975"    "N/A"     "1975"   

[[2]]
[1] "Alaska"    "10/1/1994" "0.252"     "1995"     

[[3]]
[1] "Arizona"    " 7/17/1994" "0.460"      "1995"      

[[4]]
[1] "Arkansas"   " 7/27/1995" "0.433"      "1996"      

[[5]]
[1] "California" "N/A"        "N/A"        "0"         

[[6]]
[1] "Colorado"   " 5/17/2003" "0.627"      "2003"

Great! Now we want to put our data in tibble format. To do so we need to bind the rows together. We can do so using the base rbind() function. We will use this instead of the bind_rows() function of dplyr because rbind() is less restrictive and allows for columns without names. We will use the base do.call() function, so that this is performed along each character string within the list of p_62b while maintaining the structure. Then we create a tibble out of this.

# A tibble: 52 x 2
   STATE                RTC_LAW_YEAR
   <chr>                       <dbl>
 1 Alabama                      1975
 2 Alaska                       1995
 3 Arizona                      1995
 4 Arkansas                     1996
 5 California                    Inf
 6 Colorado                     2003
 7 Connecticut                  1970
 8 Delaware                      Inf
 9 District of Columbia          Inf
10 Florida                      1988
# … with 42 more rows

Joining Data

Now we will join the data from the different data sets together to create a tibble of data for an analysis that will be similar to the data used by Donohue et al. {target="_blank"} and Mustard and Lott.

First we need to check that our data is indeed ready to be joined. We need to make sure that the column names are the same for each dataset that we intend to combine together.

We will use the compare_df_cols() and compare_df_cols_same() functions of the janitor package, to ensure that the column names are the same and that the column values are the same type so that the tibbles can be joined by row.

If they can be joined by row, then compare_df_cols_same() returns the value TRUE, while compare_df_cols(), provides a description of the columns.

[1] TRUE
  column_name data_list_1 data_list_2 data_list_3 data_list_4 data_list_5
1       STATE   character   character   character   character   character
2       VALUE     numeric     numeric     numeric     numeric     numeric
3    VARIABLE   character   character   character   character   character
4        YEAR     numeric     numeric     numeric     numeric     numeric
  data_list_6 data_list_7
1   character   character
2     numeric     numeric
3   character   character
4     numeric     numeric
[[1]]
[1] 51

[[2]]
[1] 51

[[3]]
[1] 51

[[4]]
[1] 51

[[5]]
[1] 51

[[6]]
[1] 51

[[7]]
[1] 51
[[1]]
[1] 34

[[2]]
[1] 34

[[3]]
[1] 34

[[4]]
[1] 44

[[5]]
[1] 39

[[6]]
[1] 38

[[7]]
[1] 38

Donohue, et al.

We will now bind the demographic data that we made for the Donohue-like analysis called dem_DONOHUE, as well as all the other datasets that we have wrangled. This is possible because we have the same column names for each dataset. We will also use the pivot_wider() function of the tidyr package to change the shape of the data. This will make the data have more columns. Each unique value in the column called VARIABLE will be used to make new columns. and the values for each will come from the column called VALUE.

# A tibble: 6 x 4
   YEAR STATE   VARIABLE                    VALUE
  <dbl> <chr>   <chr>                       <dbl>
1  1977 Alabama Black_Male_15_to_19_years  1.55  
2  1977 Alabama Black_Male_20_to_39_years  3.04  
3  1977 Alabama Other_Male_15_to_19_years  0.0178
4  1977 Alabama Other_Male_20_to_39_years  0.0642
5  1977 Alabama White_Male_15_to_19_years  3.58  
6  1977 Alabama White_Male_20_to_39_years 11.1   
Rows: 10
Columns: 13
$ YEAR                      <dbl> 2010, 2015, 1984, 2005, 1994, 1997, 2002, 2…
$ STATE                     <chr> "Idaho", "Maryland", "Pennsylvania", "Oklah…
$ Black_Male_15_to_19_years <dbl> 0.03964491, NA, 0.43354272, 0.35769066, 0.3…
$ Black_Male_20_to_39_years <dbl> 0.1484616, NA, 1.3569369, 1.1808047, 1.2120…
$ Other_Male_15_to_19_years <dbl> 0.24595119, NA, 0.04368943, 0.72840055, 0.0…
$ Other_Male_20_to_39_years <dbl> 0.7617805, NA, 0.1766959, 2.1102143, 0.2001…
$ White_Male_15_to_19_years <dbl> 3.453689, NA, 3.570583, 2.710170, 3.296786,…
$ White_Male_20_to_39_years <dbl> 12.563620, NA, 13.988439, 10.424571, 13.967…
$ Unemployment_rate         <dbl> 9.0, 5.1, 9.1, 4.5, 4.9, 5.3, 5.6, 9.1, 5.9…
$ Poverty_rate              <dbl> 13.8, 9.6, 15.6, 15.6, 13.7, 9.3, 11.3, 8.6…
$ Viol_crime_count          <dbl> 3464, NA, 38154, 18044, 30205, 39673, 3419,…
$ Population                <dbl> 1571450, NA, 11815214, 3548597, 5745626, 80…
$ police_per_100k_lag       <dbl> 309.3958, NA, 224.4140, 313.7014, 245.0560,…

We will also add the Right to Carry Law data using the left_join() function of the dplyr package. Which will place the DONOHUE_DF data on the left of the RTC data. Values will be matched by STATE. Then we will create a new variable called RTC_LAW using the mutate() function and the case_when() function of the dplyr package that will have the value TRUE if the current year data is equal to or greater than the year that a more permissive RTC law was adopted, otherwise the value will be FALSE.

# A tibble: 6 x 2
  STATE      RTC_LAW_YEAR
  <chr>             <dbl>
1 Alabama            1975
2 Alaska             1995
3 Arizona            1995
4 Arkansas           1996
5 California          Inf
6 Colorado           2003
Rows: 10
Columns: 15
$ YEAR                      <dbl> 1994, 1981, 2010, 1981, 1978, 2000, 2004, 2…
$ STATE                     <chr> "South Carolina", "New Hampshire", "Alabama…
$ Black_Male_15_to_19_years <dbl> 1.36816043, 0.03010794, 1.16118160, 1.66919…
$ Black_Male_20_to_39_years <dbl> 4.5249963, 0.1174423, 3.4773383, 4.1836084,…
$ Other_Male_15_to_19_years <dbl> 0.04012049, 0.02508995, 0.13236375, 0.04916…
$ Other_Male_20_to_39_years <dbl> 0.1678460, 0.1124243, 0.5047335, 0.1724133,…
$ White_Male_15_to_19_years <dbl> 2.278440, 4.476367, 2.346186, 3.181580, 4.5…
$ White_Male_20_to_39_years <dbl> 10.964430, 16.536733, 8.980486, 11.801055, …
$ Unemployment_rate         <dbl> 6.2, 5.2, 10.5, 8.4, 3.1, 3.0, 5.9, 8.1, 5.…
$ Poverty_rate              <dbl> 13.8, 7.6, 17.2, 22.0, NA, 10.4, 12.2, 12.5…
$ Viol_crime_count          <dbl> 37756, 1375, 18363, 27478, 7471, 523, 28226…
$ Population                <dbl> 3666456, 936630, 4785298, 4283312, 2333141,…
$ police_per_100k_lag       <dbl> 291.9440, 205.4173, 348.2751, 239.9778, 206…
$ RTC_LAW_YEAR              <dbl> 1997, 1959, 1975, 1996, 2007, 1986, 2004, 1…
$ RTC_LAW                   <lgl> FALSE, TRUE, TRUE, FALSE, FALSE, TRUE, TRUE…

Since we have differing numbers of years for each data set, we can use the drop_na() function of the tidyr package. to remove years that have incomplete data. Thus any row with NA values will be removed.

For example, we can see that for 1977, although we have most of the data, we do not have the poverty rate.

Rows: 6
Columns: 15
$ YEAR                      <dbl> 1977, 1977, 1977, 1977, 1977, 1977
$ STATE                     <chr> "Alabama", "Alaska", "Arizona", "Arkansas",…
$ Black_Male_15_to_19_years <dbl> 1.5457212, 0.1631338, 0.1804065, 1.0106946,…
$ Black_Male_20_to_39_years <dbl> 3.0379602, 0.9684809, 0.4796284, 1.8185525,…
$ Other_Male_15_to_19_years <dbl> 0.01781857, 1.11902724, 0.40213472, 0.03058…
$ Other_Male_20_to_39_years <dbl> 0.06421558, 2.72594532, 0.89144464, 0.09260…
$ White_Male_15_to_19_years <dbl> 3.578069, 3.828357, 4.391965, 3.879766, 4.1…
$ White_Male_20_to_39_years <dbl> 11.08537, 17.91501, 14.07430, 11.70744, 14.…
$ Unemployment_rate         <dbl> 7.3, 9.9, 8.2, 6.5, 8.3, 6.4
$ Poverty_rate              <dbl> NA, NA, NA, NA, NA, NA
$ Viol_crime_count          <dbl> 15293, 1804, 11347, 6924, 154582, 13407
$ Population                <dbl> 3782571, 397220, 2427296, 2207195, 22350332…
$ police_per_100k_lag       <dbl> 195.1054, 136.9518, 264.2447, 151.5045, 293…
$ RTC_LAW_YEAR              <dbl> 1975, 1995, 1995, 1996, Inf, 2003
$ RTC_LAW                   <lgl> TRUE, FALSE, FALSE, FALSE, FALSE, FALSE

Another example, in 2018 we only have information about unemployment rates, poverty rates, and RTC laws.

Rows: 6
Columns: 15
$ YEAR                      <dbl> 2018, 2018, 2018, 2018, 2018, 2018
$ STATE                     <chr> "Alabama", "Alaska", "Arizona", "Arkansas",…
$ Black_Male_15_to_19_years <dbl> NA, NA, NA, NA, NA, NA
$ Black_Male_20_to_39_years <dbl> NA, NA, NA, NA, NA, NA
$ Other_Male_15_to_19_years <dbl> NA, NA, NA, NA, NA, NA
$ Other_Male_20_to_39_years <dbl> NA, NA, NA, NA, NA, NA
$ White_Male_15_to_19_years <dbl> NA, NA, NA, NA, NA, NA
$ White_Male_20_to_39_years <dbl> NA, NA, NA, NA, NA, NA
$ Unemployment_rate         <dbl> 3.9, 6.5, 4.7, 3.6, 4.3, 3.2
$ Poverty_rate              <dbl> 16.0, 13.1, 12.8, 15.9, 11.9, 9.1
$ Viol_crime_count          <dbl> NA, NA, NA, NA, NA, NA
$ Population                <dbl> NA, NA, NA, NA, NA, NA
$ police_per_100k_lag       <dbl> NA, NA, NA, NA, NA, NA
$ RTC_LAW_YEAR              <dbl> 1975, 1995, 1995, 1996, Inf, 2003
$ RTC_LAW                   <lgl> TRUE, TRUE, TRUE, TRUE, FALSE, TRUE
Rows: 6
Columns: 15
$ YEAR                      <dbl> 1980, 1980, 1980, 1980, 1980, 1980
$ STATE                     <chr> "Alabama", "Alaska", "Arizona", "Arkansas",…
$ Black_Male_15_to_19_years <dbl> 1.4567383, 0.1670456, 0.1747544, 0.9545139,…
$ Black_Male_20_to_39_years <dbl> 3.3613348, 0.9933775, 0.5267121, 1.9738213,…
$ Other_Male_15_to_19_years <dbl> 0.02128385, 1.12978156, 0.41504620, 0.03849…
$ Other_Male_20_to_39_years <dbl> 0.08608419, 2.96332905, 0.98492602, 0.12425…
$ White_Male_15_to_19_years <dbl> 3.398210, 3.627805, 4.091577, 3.740199, 3.8…
$ White_Male_20_to_39_years <dbl> 11.57164, 18.28852, 14.69238, 12.12513, 14.…
$ Unemployment_rate         <dbl> 8.9, 9.6, 6.6, 7.6, 6.8, 5.8
$ Poverty_rate              <dbl> 21.2, 9.6, 12.8, 21.5, 11.0, 8.6
$ Viol_crime_count          <dbl> 17320, 1919, 17673, 7656, 210290, 15215
$ Population                <dbl> 3899671, 404680, 2735840, 2288809, 23792840…
$ police_per_100k_lag       <dbl> 201.3247, 194.7218, 262.6616, 152.0005, 243…
$ RTC_LAW_YEAR              <dbl> 1975, 1995, 1995, 1996, Inf, 2003
$ RTC_LAW                   <lgl> TRUE, FALSE, FALSE, FALSE, FALSE, FALSE
Rows: 6
Columns: 15
$ YEAR                      <dbl> 2010, 2010, 2010, 2010, 2010, 2010
$ STATE                     <chr> "Vermont", "Virginia", "Washington", "West …
$ Black_Male_15_to_19_years <dbl> 0.06725669, 0.83162848, 0.15964128, 0.16343…
$ Black_Male_20_to_39_years <dbl> 0.2056042, 2.7300368, 0.6653574, 0.6164599,…
$ Other_Male_15_to_19_years <dbl> 0.1702984, 0.3274050, 0.5979246, 0.1050716,…
$ Other_Male_20_to_39_years <dbl> 0.4754297, 1.3483385, 2.1186016, 0.3126259,…
$ White_Male_15_to_19_years <dbl> 3.531216, 2.338080, 2.755299, 3.050422, 2.9…
$ White_Male_20_to_39_years <dbl> 11.363026, 9.776093, 11.195603, 11.548874, …
$ Unemployment_rate         <dbl> 6.1, 7.1, 10.0, 8.7, 8.7, 6.4
$ Poverty_rate              <dbl> 10.8, 10.7, 11.6, 16.8, 10.1, 9.6
$ Viol_crime_count          <dbl> 820, 17184, 21138, 5586, 14167, 1117
$ Population                <dbl> 625960, 8024617, 6744496, 1853973, 5691047,…
$ police_per_100k_lag       <dbl> 264.2341, 302.9677, 265.1495, 272.0644, 344…
$ RTC_LAW_YEAR              <dbl> 1970, 1995, 1961, 1990, 2012, 1995
$ RTC_LAW                   <lgl> TRUE, TRUE, TRUE, TRUE, FALSE, TRUE

Now we have complete data and the data spans from 1980 to 2010.

 [1] 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994
[16] 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
[31] 2010

If we include states that had a RTC law adopted before our time span of data, say 1975, then we only have information about crime rates and the other variables of interest after the law was adopted but not before, therefore including these states doesn’t really makes sense. Thus, we will drop the data for these states. We can use the set_diff() function of the dplyr package to see what states are in the population_data that contains all the original 51 states (recall this includes the District of Columbia) but are not in the DONOHUE_DF. The order matters here. If we did it the other way around with population_data listed second, then set_diff would test if there are any states in Donohue_DF that are not in population_data. As there are none this would result in nothing.

# A tibble: 6 x 1
  STATE        
  <chr>        
1 Alabama      
2 Connecticut  
3 Indiana      
4 New Hampshire
5 Vermont      
6 Washington

We will also calculate a violent crime rate relative to the population in that state at that time, now that we have data for both crime count and population. Will will also calculate the log value of this rate and the population.

Mustard and Lott

We will now bind the demographic data that we made for the Mustard and Lott analysis called dem_Lott, as well as all the other datasets that we have wrangled just as we did for the Donohue-like analysis. Again, this is possible because we have the same column names for each dataset.

# A tibble: 6 x 1
  STATE        
  <chr>        
1 Alabama      
2 Connecticut  
3 Indiana      
4 New Hampshire
5 Vermont      
6 Washington

Let’s see how the data compares:

We will check the dimensions of each using the base dim() function

[1] 1395   50
[1] 1395   20

As expected the Lott_DF is 30 columns larger, due to the 30 additional demographic variables. We can check those now as well.

 [1] "YEAR"                           "STATE"                         
 [3] "Black_Female_10_to_19_years"    "Black_Female_20_to_29_years"   
 [5] "Black_Female_30_to_39_years"    "Black_Female_40_to_49_years"   
 [7] "Black_Female_50_to_64_years"    "Black_Female_65_years_and_over"
 [9] "Black_Male_10_to_19_years"      "Black_Male_20_to_29_years"     
[11] "Black_Male_30_to_39_years"      "Black_Male_40_to_49_years"     
[13] "Black_Male_50_to_64_years"      "Black_Male_65_years_and_over"  
[15] "Other_Female_10_to_19_years"    "Other_Female_20_to_29_years"   
[17] "Other_Female_30_to_39_years"    "Other_Female_40_to_49_years"   
[19] "Other_Female_50_to_64_years"    "Other_Female_65_years_and_over"
[21] "Other_Male_10_to_19_years"      "Other_Male_20_to_29_years"     
[23] "Other_Male_30_to_39_years"      "Other_Male_40_to_49_years"     
[25] "Other_Male_50_to_64_years"      "Other_Male_65_years_and_over"  
[27] "White_Female_10_to_19_years"    "White_Female_20_to_29_years"   
[29] "White_Female_30_to_39_years"    "White_Female_40_to_49_years"   
[31] "White_Female_50_to_64_years"    "White_Female_65_years_and_over"
[33] "White_Male_10_to_19_years"      "White_Male_20_to_29_years"     
[35] "White_Male_30_to_39_years"      "White_Male_40_to_49_years"     
[37] "White_Male_50_to_64_years"      "White_Male_65_years_and_over"  
[39] "Unemployment_rate"              "Poverty_rate"                  
[41] "Viol_crime_count"               "Population"                    
[43] "police_per_100k_lag"            "RTC_LAW_YEAR"                  
[45] "RTC_LAW"                        "TIME_0"                        
[47] "TIME_INF"                       "Viol_crime_rate_1k"            
[49] "Viol_crime_rate_1k_log"         "Population_log"                
 [1] "YEAR"                      "STATE"                    
 [3] "Black_Male_15_to_19_years" "Black_Male_20_to_39_years"
 [5] "Other_Male_15_to_19_years" "Other_Male_20_to_39_years"
 [7] "White_Male_15_to_19_years" "White_Male_20_to_39_years"
 [9] "Unemployment_rate"         "Poverty_rate"             
[11] "Viol_crime_count"          "Population"               
[13] "police_per_100k_lag"       "RTC_LAW_YEAR"             
[15] "RTC_LAW"                   "TIME_0"                   
[17] "TIME_INF"                  "Viol_crime_rate_1k"       
[19] "Viol_crime_rate_1k_log"    "Population_log"

Lastly, we will check that the YEAR values are the same. We can use the setequal() function of the dplyr package to see if the values are the same.

[1] TRUE

Looks as expected! we have

from Michael:

Now we will save our wrangled data for the part 2 case study:

We can use the here() function of the here package to easily save this in a directory called data within the directory where are .Rproj file is located.

Summary

This case study has introduced many concepts for data importation and data wrangling. To continue with this data to see more about data analysis and visualization see this next case study.

Homework

Ask students to import and wrangle similar datasets to those used here.

Additional Information

Session Info

R version 4.0.3 (2020-10-10)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Mojave 10.14.5

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] janitor_2.0.1  tibble_3.0.4   forcats_0.5.0  purrr_0.3.4    stringr_1.4.0 
 [6] tidyr_1.1.0    magrittr_2.0.1 dplyr_1.0.2    pdftools_2.3.1 readr_1.3.1   
[11] readxl_1.3.1   knitr_1.28     here_0.1      

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.4.6     cellranger_1.1.0 pillar_1.4.7     compiler_4.0.3  
 [5] base64enc_0.1-3  tools_4.0.3      digest_0.6.27    lubridate_1.7.8 
 [9] evaluate_0.14    lifecycle_0.2.0  pkgconfig_2.0.3  rlang_0.4.8     
[13] cli_2.2.0        yaml_2.2.1       xfun_0.14        generics_0.1.0  
[17] vctrs_0.3.5      askpass_1.1      hms_0.5.3        rprojroot_1.3-2 
[21] tidyselect_1.1.0 snakecase_0.11.0 glue_1.4.2       qpdf_1.1        
[25] R6_2.5.0         rematch_1.0.1    fansi_0.4.1      rmarkdown_2.2   
[29] backports_1.1.7  ellipsis_0.3.1   htmltools_0.5.0  assertthat_0.2.1
[33] utf8_1.1.4       stringi_1.5.3    crayon_1.3.4

Acknowledgments

We would like to acknowledge Daniel Webster for assisting in framing the major direction of the case study.

We would also like to acknowledge the Bloomberg American Health Initiative for funding this work.

---
title: "Influence of Multicollinearity on Measured Impact of Right-to-Carry Gun Laws Part 1"
css: style.css
output:
  html_document:
    self_contained: yes
    code_download: yes
    highlight: tango
    number_sections: no
    theme: cosmo
    toc: yes
    toc_float: yes
  pdf_document:
    toc: yes
  word_document:
    toc: yes
---

<style>
#TOC {
  background: url("https://opencasestudies.github.io/img/icon-bahi.png");
  background-size: contain;
  padding-top: 240px !important;
  background-repeat: no-repeat;
}
</style>


---


```{r setup, include=FALSE}
knitr::opts_chunk$set(include = TRUE, comment = NA, echo = TRUE,
                      message = FALSE, warning = FALSE, cache = FALSE, fig.width=10, fig.height=7,
                      fig.align = "center", out.width = '90%')
library(here)
library(knitr)

```




#### {.outline }
```{r, echo = FALSE, out.width = "800 px"}
knitr::include_graphics(here::here("img", "mainplot.png"))
```

####

#### {.disclaimer_block}

**Disclaimer**: The purpose of the [Open Case Studies](https://opencasestudies.github.io){target="_blank"} project is **to demonstrate the use of various data science methods, tools, and software in the context of messy, real-world data**. A given case study does not cover all aspects of the research process, is not claiming to be the most appropriate way to analyze a given data set, and should not be used in the context of making policy decisions without external consultation from scientific experts. 
####

#### {.license_block}

This work is licensed under the Creative Commons Attribution-NonCommercial 3.0 [(CC BY-NC 3.0)](https://creativecommons.org/licenses/by-nc/3.0/us/){target="_blank"}  United States License.

####

#### {.reference_block}

To cite this case study please use:

Wright, Carrie and Ontiveros, Michael and Jager, Leah and Taub, Margaret and Hicks, Stephanie. (2020). https://github.com/opencasestudies/ocs-bp-RTC-wrangling.  Influence of Multicollinearity on Measured Impact of Right-to-Carry Gun Laws Part 1 (Version v1.0.0).

####

To access the GitHub repository for this case study see here: https://github.com/opencasestudies/ocs-bp-RTC-wrangling.   
This case study is part of a series of public health case studies for the [Bloomberg American Health Initiative](https://americanhealth.jhu.edu/open-case-studies).   
See [this case study](https://github.com/opencasestudies/ocs-bp-RTC-analysis) for part 2 which includes a data analysis and information about data visualization.  



# **Motivation**
*** 

This case study shows the wrangling performed for another [case study](https://www.opencasestudies.org/ocs-bp-RTC-analysis/).

This other case study introduces the topic of [multicollinearity](https://en.wikipedia.org/wiki/Multicollinearity){target="_blank"}, which occurs in regression when one or more independent variables can be predicted by other independent variables. 

It does so by showcasing a real world example where multicollinearity in part resulted in historically controversial and conflicting findings about the influence of the adoption of right-to-carry (RTC) concealed handgun laws on violent crime rates in the United States.  

We will focus on two articles:

1. The first analysis by [Mustard and Lott](https://chicagounbound.uchicago.edu/cgi/viewcontent.cgi?article=1150&context=law_and_economics){target="_blank"} published in 1996 suggests that RTC laws reduce violent crime. Lott authored a book extending these findings in 1998 called [***More Guns, Less Crime***](https://en.wikipedia.org/wiki/More_Guns,_Less_Crime){target="_blank"}.

```{r, echo=FALSE, out.height = '100%', out.width = '100%', fig.align='center'}
knitr::include_graphics(here("img", "Lott.png"))
```

##### [[source]](https://chicagounbound.uchicago.edu/cgi/viewcontent.cgi?article=1150&context=law_and_economics){target="_blank"}

2. The second analysis is a recent article by [Donohue, et al.](https://www.nber.org/papers/w23510.pdf){target="_blank"} published in 2017 that suggests that RTC laws increase violent crime. Donohue has also published previous articles with titles such as [***Shooting down the "More Guns, Less Crime" Hypothesis***](https://www.jstor.org/stable/1229603?seq=1){target="_blank"}. 

```{r, echo=FALSE, out.height = '100%', out.width = '100%', fig.align='center'}
knitr::include_graphics(here("img", "Donohue.png"))
```

##### [[source]](https://www.nber.org/papers/w23510.pdf){target="_blank"}

This has been a controversial topic as many other analyses also produced conflicting results. See [here](https://en.wikipedia.org/wiki/More_Guns,_Less_Crime){target="_blank"} for a list of studies.

The [Donohue, et al.](https://www.nber.org/papers/w23510.pdf){target="_blank"} article discusses how there are many other important methodological aspects besides [multicollinearity](https://en.wikipedia.org/wiki/Multicollinearity){target="_blank"} (which occurs when predictor or input variables are highly related in a regression analysis) that could account for the historically conflicting results in these previous manuscripts.

In fact, nearly every aspect of the data analysis process was different between the [Donohue, et al.](https://www.nber.org/papers/w23510.pdf){target="_blank"} and [Mustard and Lott](https://chicagounbound.uchicago.edu/cgi/viewcontent.cgi?article=1150&context=law_and_economics){target="_blank"} analyses.
```{r, echo=FALSE, out.height = '75%', out.width = '75%', fig.align='center'}
knitr::include_graphics(here("img", "Educational_Graphic1.jpg"))
```


However, we will focus particularly on multicollinearity and how it can influence the results we get from linear regression. 
Specifically, this analysis will demonstrate how methodological details can be critically influential for our overall conclusions and can result in important policy related consequences. The [Donohue, et al. article]((https://www.nber.org/papers/w23510.pdf){target="_blank"}) will provide a basis for the motivation. 

#### {.reference_block}

John J. Donohue et al., Right‐to‐Carry Laws and Violent Crime: A Comprehensive Assessment Using Panel Data and a State‐Level Synthetic Control Analysis. *Journal of Empirical Legal Studies*, 16,2 (2019).

David B. Mustard & John Lott. Crime, Deterrence, and Right-to-Carry Concealed Handguns. *Coase-Sandor Institute for Law & Economics* Working Paper No. 41, (1996).

####


Before we leave this section, we provide a high-level overview of what variables were (or were not) included in the [Donohue, Aneja and Weber](https://www.nber.org/papers/w23510.pdf){target="_blank"}) (DAW) paper and the [Mustard and Lott](https://chicagounbound.uchicago.edu/cgi/viewcontent.cgi?article=1150&context=law_and_economics){target="_blank"} (ML) paper:


```{r, echo=FALSE, out.height = '100%', out.width = '100%', fig.align='center'}
knitr::include_graphics(here("img",'Donohue_Table2_edited.png'))
```

##### [[source]](https://www.nber.org/papers/w23510.pdf){target="_blank"}


###### *ML is abbreviated as LM in the source article

**Note**: We are not attempting to re-create the analyses from the original authors. Instead, we aim to use a subset of the listed explanatory variables in this case study to demonstrate multicollinearity. These variables will be consistent for both analyses that we will perform, with the exception that one analysis will have 6 demographic variables as in the analysis in the [Donohue, et al.](https://www.nber.org/papers/w23510.pdf){target="_blank"} article and the other will have 36 demographic variables, grouping individuals into more specific categories, as in the analysis in the [Mustard and Lott](https://chicagounbound.uchicago.edu/cgi/viewcontent.cgi?article=1150&context=law_and_economics){target="_blank"} article.



# **Main Question**
*** 

#### {.main_question_block}
<b><u> Our main question: </u></b>

What is the effect of multicollinearity on coefficient estimates from linear regression models when analyzing right to carry laws and violence rates?

####


Specifically, we will consider the two ways to define the demographic variables (as described above) and investigate how the inclusion of different numbers of age groups influences the results of an analysis of right to carry laws and violence rates

In this case study we only demonstrate how to import and wrangle the data.

# **Learning Objectives** 
*** 

<u>**Data Science Learning Objectives:**</u>

1. Data import of many different file types with special cases (`readr`, `readxl`, `pdftools`)  
2. Joining data from multiple sources (`dplyr`)  
3. Working with character strings (`stringr`)  
4. Data comparisons (`dplyr` and `janitor`)  
5. Reshaping data into different formats (`tidyr`)  


We will especially focus on using packages and functions from the [`tidyverse`](https://www.tidyverse.org/){target="_blank"}, such as `dplyr` and `ggplot2`. The tidyverse is a library of packages created by RStudio. While some students may be familiar with previous R programming packages, these packages make data science in R especially legible.

```{r, out.width = "20%", echo = FALSE, fig.align ="center"}
include_graphics("https://tidyverse.tidyverse.org/logo.png")
```

# **Context**
***

So what exactly is a **right-to-carry law**?

It is a law that specifies _if_ and _how_ citizens are allowed to have a firearm on their person or nearby (for example, in a citizen's car) in public. 

The [Second Amendment](https://en.wikipedia.org/wiki/Second_Amendment_to_the_United_States_Constitution){target="_blank"} to the United States Constitution guarantees the right to "keep and bear arms". The amendment was ratified in 1791 as part of the [Bill of Rights](https://en.wikipedia.org/wiki/United_States_Bill_of_Rights){target="_blank"}.

```{r, echo=FALSE, out.height = '50%', out.width = '50%', fig.align='center'}
knitr::include_graphics("https://upload.wikimedia.org/wikipedia/commons/7/79/Bill_of_Rights_Pg1of1_AC.jpg")
```

##### [[source]](https://upload.wikimedia.org/wikipedia/commons/7/79/Bill_of_Rights_Pg1of1_AC.jpg){target="_blank"}

However, there are no federal laws about carrying firearms in public. 

These laws are created and enforced at the US state level. 
States vary greatly in their laws about the right to carry firearms. 
Some require extensive effort to obtain a permit to legally carry a firearm, while other states require very minimal effort to do so.

<details> <summary> Click here for more information on history of right-to-carry policies in the US. </summary>

According to the [Wikipedia entry](https://en.wikipedia.org/wiki/History_of_concealed_carry_in_the_U.S.){target="_blank"} about the history of right-to-carry policies in the United States:

> Public perception on concealed carry vs open carry has largely flipped. In the early days of the United States, open carrying of firearms, long guns and revolvers was a common and well-accepted practice. Seeing guns carried openly was not considered to be any cause for alarm. Therefore, anyone who would carry a firearm but attempt to conceal it was considered to have something to hide, and presumed to be a criminal. For this reason, concealed carry was denounced as a detestable practice in the early days of the United States.

> Concealed weapons bans were passed in Kentucky and Louisiana in 1813. (In those days open carry of weapons for self-defense was considered acceptable; concealed carry was denounced as the practice of criminals.) By 1859, Indiana, Tennessee, Virginia, Alabama, and Ohio had followed suit. By the end of the nineteenth century, similar laws were passed in places such as Texas, Florida, and Oklahoma, which protected some gun rights in their state constitutions. Before the mid 1900s, most U.S. states had passed concealed carry laws rather than banning weapons completely. Until the late 1990s, many Southern states were either "No-Issue" or "Restrictive May-Issue". Since then, these states have largely enacted "Shall-Issue" licensing laws, with numerous states legalizing "Unrestricted concealed carry".

</details>

There are five broad categories of right-to-carry laws:

```{r, echo=FALSE, out.height = '100%', out.width = '100%', fig.align='center'}
knitr::include_graphics(here("img", "RTC.png"))
```

##### [[source]](https://www.nraila.org/gun-laws/){target="_blank"}

```{r, echo=FALSE, out.height = '100%', out.width = '100%', fig.align='center'}
knitr::include_graphics(here("img", "RTC_map.png"))
```

##### [[source]](https://www.nraila.org/gun-laws/){target="_blank"}

You can see that no state in the US currently (this map is from 2020) has a "Rights Infringed/Non-Issue" law (the gray category) -- meaning that all 50 states in the US allow the right to carry firearms at least in some way. 
However the level of restrictions is dramatically different from one state to another.


<details> <summary> Click here for more information about how restrictions vary from one state to another. </summary>

There is variation from state to state even within the same general category:

For example here is an abridged version of the [current carry laws in Idaho](https://www.nraila.org/gun-laws/state-gun-laws/idaho/) which is considered an "Unrestricted - no permit required" state:

> State law ... allows any resident of Idaho or a current member of the armed forces of the United States to carry a concealed handgun without a license to carry, provided the person is over 18 years old and not disqualified from being issued a license to carry concealed weapons under state law. An amendment to state law that takes effect on July 1, 2020 changes the reference in the above law from “a resident of Idaho” to “any citizen of the United States.”  


And here are is an abridged version of the [current carry laws in Arizona](https://www.nraila.org/gun-laws/state-gun-laws/arizona/) which is also considered an "Unrestricted - no permit required" state:

> Any person 21 years of age or older, who is not prohibited possessor, may carry a weapon openly or concealed without the need for a license...

Notice that citizens in Idaho only need to be 18 to carry a firearm, whereas they must be 21 in Arizona. 

</details>


# **Limitations**
*** 

There are some important considerations regarding this data analysis to keep in mind: 

1. We do not use all of the data used by either the [Mustard and Lott](https://chicagounbound.uchicago.edu/cgi/viewcontent.cgi?article=1150&context=law_and_economics){target="_blank"} or [Donohue, et al.](https://www.nber.org/papers/w23510.pdf){target="_blank"} analyses, nor do we perform the same analysis as in each article. We instead perform a much simpler analysis with fewer variables for the purposes of illustration of the concept of multicollinearity and its influence on regression coefficients, not to reproduce either analysis.

2. Our analysis accounts for either the adoption or lack of adoption of a permissive right-to-carry law in each state, but does not account for differences in the level of permissiveness of the laws.

Recall that these are the categories of right to carry laws:
```{r, echo=FALSE, out.height = '100%', out.width = '100%', fig.align='center'}
knitr::include_graphics(here("img", "RTC.png"))
```

States with laws of the category rights restricted - very limited issue (red) are considered as not having a permissive right-to-carry law. Recall that no states currently have a rights infringed/non-issue law.

States of all other categories (shall issue, discretionary/reasonable issue, and no permit required, all shades of blue) are considered the same in our analysis, as having a permissive right-to-carry law.

3) Because our analysis in the next [case study](https://www.opencasestudies.org/ocs-bp-RTC-analysis) is an oversimplification, the results presented here should not be used for determining policy changes; instead we suggest that users interested in such a determination consult with a specialist.

4) The inclusion of race as an explanatory variable in an epidemiological study can be useful in certain circumstances. However, there are limitations and issues around defining, determining, and reporting race, as well as in interpreting differences in public health outcomes by race. For more information on this topic, we have included a [link](https://academic.oup.com/epirev/article/22/2/187/456942) to a paper on the use of race as a measure in epidemiology. We include race in this analysis to demonstrate and consider the limitations of what the previous papers have done to analyze the influence of RTC laws on violent crime, with a focus on multicollinearity. Thus in our analysis we have also defined race as was previously done in these papers. Furthermore, we want to point out that reporting analyses about crime with race as a variable can have very unexpected consequences and thus care should be taken. See [here](https://journals.sagepub.com/doi/full/10.1177/0963721418763931) for suggestions. Any association between demographic variables (indicating the proportion of the population from specific race and age groups) and violent crime does not necessarily indicate that the two are linked causally, as aside from the issues presented in the [article]((https://academic.oup.com/epirev/article/22/2/187/456942)), this may instead indicate higher rates of police engagement with certain racial groups due to [racial profling](https://www.aclu.org/other/racial-profiling-definition).

The ACLU defines racial profiling as:

>"Racial Profiling" refers to the discriminatory practice by law enforcement officials of targeting individuals for suspicion of crime based on the individual's race, ethnicity, religion or national origin.

***



We will begin by loading the packages that we will need:

```{r}
library(here)
library(readxl)
library(readr)
library(pdftools)
library(dplyr)
library(magrittr)
library(tidyr)
library(stringr)
library(purrr)
library(forcats)
library(tibble)
```

<u>**Packages used in this case study:** </u>

  Package   | Use in this case study                                                                        
---------- |-------------
[here](https://github.com/jennybc/here_here){target="_blank"}       | to easily load and save data
[readxl](https://readxl.tidyverse.org/){target="_blank"}      | to import the data in the excel files 
[readr](https://readr.tidyverse.org/){target="_blank"}      | to import the CSV file data
[pdftools](https://github.com/ropensci/pdftools){target="_blank"} | to import data from a pdf file
[dplyr](https://dplyr.tidyverse.org/){target="_blank"}      | to arrange/filter/select/compare specific subsets of the data  
[magrittr](https://cran.r-project.org/web/packages/magrittr/vignettes/magrittr.html){target="_blank"} | to use the compound assignment pipe operator `%<>%`
[tidyr](https://tidyr.tidyverse.org/){target="_blank"}      | to rearrange data in wide and long formats 
[stringr](https://stringr.tidyverse.org/articles/stringr.html){target="_blank"}    | to manipulate the character strings within the data  
[purrr](https://purrr.tidyverse.org/){target="_blank"}   | to import the data in all the different excel and csv files efficiently
[forcats](https://forcats.tidyverse.org/){target="_blank"}    | to allow for reordering of factors in plots
[tibble](https://tibble.tidyverse.org/){target="_blank"}     | to create data objects that we can manipulate with `dplyr`/`stringr`/`tidyr`/`purrr`

# **What are the data?**
***

Below is a table from the [Donohue, et al.](https://www.nber.org/papers/w23510.pdf){target="_blank"} paper that shows the data used in both analyses, where DAW stands for [Donohue, et al.](https://www.nber.org/papers/w23510.pdf){target="_blank"} and ML stands for [Mustard and Lott](https://chicagounbound.uchicago.edu/cgi/viewcontent.cgi?article=1150&context=law_and_economics){target="_blank"}.


```{r, echo=FALSE, out.height = '100%', out.width = '100%', fig.align='center'}
knitr::include_graphics(here("img", "Donohue_AppendixJ.png"))
```

We will be using a subset of these variables, which are highlighted in green:


```{r, echo=FALSE, out.height = '100%', out.width = '100%', fig.align='center'}
knitr::include_graphics(here("img", "ourdata.png"))
```


# **Data Import**
***


## **Demographic and population data**
***
To obtain information about age, sex, and race, and overall population we will use US Census Bureau data, just like both of the articles. The census data is available for different time spans. Here are the links for the years used in our analysis. We will use data from 1977 to 2010.

Data   | Link                                                                        
---------- |-------------
**years 1977 to 1979**  | [link](https://www2.census.gov/programs-surveys/popest/tables/1900-1980/state/asrh/)  
**years 1980 to 1989**  | [link](https://www2.census.gov/programs-surveys/popest/tables/1980-1990/counties/asrh/) * county data was used for this decade which also has state information
**years 1990 to 1999**  | [link](https://www2.census.gov/programs-surveys/popest/tables/1990-2000/state/asrh/)
**years 2000 to 2010**  | [link](https://www.census.gov/data/datasets/time-series/demo/popest/intercensal-2000-2010-state.html) <br> [technical documentation](https://www2.census.gov/programs-surveys/popest/technical-documentation/file-layouts/2000-2010/intercensal/state/st-est00int-alldata.pdf){target="_blank"}

To import the data we will use the `read_csv()` function of the `readr` package for the csv files. In some decades, there are separate files for each year, we will read each of these together using the base `list.files()` function to get all of the names for each file and then the `map()` function of the `purrr` package to apply the `read_csv()` function on all of the file paths in the list created by `list.files()`. For years that are txt files we will use `read_table2()` also for the `readr` package. The `read_table2()` function, unlike the `read_table()`,  allows for any number of white space characters between columns, and the lines can be of different lengths.


```{r}

dem_77_79 <- read_csv("docs/Demographics/Decade_1970/pe-19.csv", skip = 5)

dem_80_89 <- list.files(recursive = TRUE,
                  path = "docs/Demographics/Decade_1980/",
                  pattern = "*.csv",
                  full.names = TRUE) %>% 
  map(~read_csv(., skip=5))

dem_90_99 <- list.files(recursive = TRUE,
                  path = "docs/Demographics/Decade_1990/",
                  pattern = "*.txt",
                  full.names = TRUE) %>% 
  map(~read_table2(., skip = 14))


dem_00_10_2 <- read_csv("docs/Demographics/Decade_2000/st-est00int-alldata.csv")

dem_00_10 <- list.files(recursive = TRUE,
                  path = "docs/Demographics/Decade_2000/",
                  pattern = "*.csv",
                   full.names = TRUE) %>% 
   map(~read_csv(.))

head(dem_00_10)

```

Notice that the `STATE` variable for the demographic data is numeric. That is because it is encoded by [Federal Information Processing Standard (FIPS) state codes](https://en.wikipedia.org/wiki/Federal_Information_Processing_Standard_state_code){target="_blank". Thus we also need to import data  about FIPS encoding so that we can identify what data corresponds to what state.


## **State FIPS codes**
***



The following data was downloaded from the [US Census Bureau](https://www.census.gov/geographies/reference-files/2014/demo/popest/2014-geocodes-state.html){target="_blank"}.

To import the data we will use the `read_xls()` function of the `readxl` package. Since the first five lines of this excel is information about the source of the data and when it was released, we need to skip importing these lines using the `skip` argument so that the data has the same number of columns for each row. 

```{r, out.width = "500 px"}
knitr::include_graphics(here("img", "FIPS.png"))

```

```{r}
STATE_FIPS <- read_xls("docs/State_FIPS_codes/state-geocodes-v2014.xls", skip = 5)
(STATE_FIPS)
```

## **Police staffing data**
***

The following data was downloaded from the [Federal Bureau of Investigation](https://crime-data-explorer.fr.cloud.gov/downloads-and-docs). 


The `read_csv()` function of the `readr` package guesses what the class is for each variable, but sometimes it makes mistakes. It is good to specify the class for variables if you know them. We know that we want the variables about male and female counts to be numeric. We can specify that using the `col_types =` argument. See [here](https://readr.tidyverse.org/articles/readr.html) and [here](https://cran.r-project.org/web/packages/readr/vignettes/readr.html) for more information.

```{r}
ps_data <- read_csv("docs/Police_staffing/pe_1960_2018.csv")
ps_data <- read_csv("docs/Police_staffing/pe_1960_2018.csv",
                    col_types = cols(male_total_ct = "n",
                                     female_total_ct = "n"))

ps_data <- read_csv("docs/Police_staffing/pe_1960_2018.csv",
                   col_types =  cols(male_total_ct = col_double(),
                                   female_total_ct = col_double()))
```



## **Unemployment data**
***
The following data was downloaded from the [U.S. Bureau of Labor Statistics](https://data.bls.gov/cgi-bin/dsrv?la). 

There are excel files for each state.  As you can see, there are many rows to skip to make sure that there are the same number of columns for each row. We can also see that the state name is located in a couple of the first rows. 

```{r}
knitr::include_graphics(here("img", "Unemp.png"))
```

We can also see that here if we just try to read in the files directly.

```{r}

ue_rate_data <- list.files(recursive = TRUE,
                  path = "docs/Unemployment",
                  pattern = "*.xlsx",
                  full.names = TRUE) %>% 
  map(~read_xlsx(.))
      
head(ue_rate_data)[1]
```

So now we will skip the first 10 lines. And also create a names tibble that contains only the cell with the state information.

```{r}
 
 ue_rate_data <- list.files(recursive = TRUE,
                  path = "docs/Unemployment",
                  pattern = "*.xlsx",
                  full.names = TRUE) %>% 
  map(~read_xlsx(., skip = 10))
  
head(ue_rate_data[1])
```

To get the state name for each file using the `map()` function to perform functions across all of the files, we will specifically import only a small range of cells using the `range = ` argument and then grab the cell that has state information based on it's location within the range of cells imported using `c()` and then use the base `unlist()` function to unlist the list that this creates.

```{r}
ue_rate_names <- list.files(recursive = TRUE,
                  path = "docs/Unemployment",
                  pattern = "*.xlsx",
                  full.names = TRUE) %>%
  map(~read_xlsx(., range = "B4:B6")) %>%
  map(., c(1,2)) %>%
  unlist()

ue_rate_names
```

Now we will make these values the names of the different tibbles within `ue_rate_data`.
```{r}
names(ue_rate_data) <- ue_rate_names
```

## **Poverty data**
***

Extracted from Table 21 from [US Census Bureau Poverty Data ](https://www.census.gov/data/tables/time-series/demo/income-poverty/historical-poverty-people.html)

```{r}

#**persistent warning from unknown origin** https://community.rstudio.com/t/persistent-unknown-or-uninitialised-column-warnings/64879

#solution to above is alledgedly: "In any case the suggested approach is to initialize the column"


poverty_rate_data <- read_xls("docs/Poverty/hstpov21.xls", skip=2) #This may cause initialization issue, not easily reproducible (even after restarting R)

head(poverty_rate_data)
```

We can see that this will require some wrangling to make the data more usable. 

## **Violent crime**
***

Violent crime data was obtained from [here](https://www.ucrdatatool.gov/Search/Crime/State/StatebyState.cfm) This data is a bit trickier because of spaces and `/` in the column names, thus the `read_lines()` function of the `readr` package works better than the `read_csv()` function.


```{r}
knitr::include_graphics(here("img", "crime.png"))
```

```{r}
crime_data <- read_lines("docs/Crime/CrimeStatebyState.csv", skip = 2, skip_empty_rows = TRUE)
head(crime_data)

```

We can see that this data will also require some wrangling to make it more usable. 

## **Right-to-carry data**
***

This data is extracted from table in [Donohue paper](https://www.nber.org/papers/w23510.pdf) {target="_blank"}. We will use the function `pdf_text()`  of the `pdftools` package to import the pdf document.

```{r}

if(!file.exists(here("docs", "w23510.pdf"))){
  url <- "https://www.nber.org/papers/w23510.pdf"
  utils::download.file(url, here("docs", "w23510.pdf"))
}

DAWpaper <- pdf_text(here("docs", "w23510.pdf"))

head(DAWpaper[1])

```

Again, this data will also require quite a bit of wrangling.



# **Data Wrangling**
***
## **State FIPS codes**
***

Let's first take a look at our state FIPS data to see if it needs any cleaning or reshaping. We should start with this data, because we will need to use it to wrangle some of the other data.

```{r}
head(STATE_FIPS)
```

We only need the last two columns, but we might want to rename them. The `Name` variable is vague. The variable with the FIPS code is called `State\n(FIPS)`. To get rid of the new line in this variable name and to change the `Name` variable to something more informative, we will use the `rename()` function of the `dplyr` package.  To use this function, we need to list the new name first followed by `=` and then the existing variable. We can rename multiple variables at the same time by using a comma to separate the variables we are renaming. We will use the `select()` function also of the `dplyr` package just to keep these variables, and we will filter out the rows with FIPS values of `00` with the `filter()` function, again also part of the `dplyr` package. we will specify that we want `STATEFP` values that are not equal to `00` by using this operator: `!=`. We will also use the double pipe operator `%<>%` of the `magrittr` package which allows us to use data as input and then reassign it after we perform sum functions using it.

```{r}

STATE_FIPS %<>% 
dplyr::rename( STATEFP = `State\n(FIPS)`,
                 STATE = Name) %>%
    dplyr::select(STATEFP, STATE) %>%
    dplyr::filter(STATEFP != "00")

STATE_FIPS

```

## **Demographic and population data**
***


<details><summary> Click here to see detailed information about how the demographic data was wrangled </summary>


<font size="6"> **1977-1979**</font>

***


Now let's take a look at our demographic data across the decades that we wish to study. If you have very wide data (meaning it has many columns), one way to view the data so that you can see all of the columns at the same time is to use the `glimpse()` function of the `dplyr` package. 

Taking a look at the first decade of data, we can see that the `Race/Sex Indicator` contains two types of data, the race and the sex. This does not follow the tidy data philosophy, where each cell of a tibble should only contain one piece of information. Typically one might think of using the `separate()` function of the `tidyr` package to split this variable into two. However, one of the race values is `Other races` and since this also has a space, this makes separating this data more tricky.

Instead we will use the `str_extract()` function of the `stringr` package and the `mutate()` function of the `dplyr` package. The "mutate()" will allow us to create new variables, and "str_extract()" function  will allow us to match specific patterns and pull out matches to those patterns. Therefore, if the `Race/Sex Indicator` value is `Other races male` and if we extract patterns matching either `"male"` or `"female"` which we can specify like this `pattern = "male|female"` then, the value will be `male`.

First we need to rename the `Race/Sex Indicator` variable to not have spaces so that it is compatible with the `str_extract()` function.

We also want to rename a couple of variables to be simpler and filter the data to only include the years of the data we are interested in, as well as remove some variables that we don't need like the `FIPS State Code`. We can remove variables by using the `select()` function with a `-` minus sign in front of the variable we wish to remove.

```{r}
dplyr::glimpse(dem_77_79)


dem_77_79 <- dem_77_79 %>%
  rename("race_sex" =`Race/Sex Indicator`) %>%
  mutate(SEX = str_extract(race_sex, "male|female"),
        RACE = str_extract(race_sex, "Black|White|Other"))%>%
  select(-`FIPS State Code`, -`race_sex`) %>%
  rename("YEAR" = `Year of Estimate`,
        "STATE" = `State Name`) %>%
  filter(YEAR %in% 1977:1979)

glimpse(dem_77_79)
```

That's looking pretty  good! We also want to take all the age group variables and make one variable that is the age group name and one that is the value of the population count for that age group. To do this we will use the `pivot_longer()` function of the `tidyr` package. To use this function, we need to use the `cols` argument to indicate which columns we want to pivot. We also name the new variables we will create with the `names_to` and `values_to` arguments. The `names_to` will be the name of the variable that will identify each age group and `values_to` will be the name of the variable that contains the corresponding population values.
```{r}
dem_77_79 <- dem_77_79 %>%
  pivot_longer(cols=contains("years"),
               names_to = "AGE_GROUP",
               values_to = "SUB_POP")

glimpse(dem_77_79)
```

We also want to get data about the total population for the state for each year.

To do so we can sum all the values for the `SUB_POP` variable that we just created. To do this we can use the `group_by` and `summarize()` functions of the `dplyr` package. The `group_by()` function specifies how we want to calculate  our sum, that we would like to calculate it for each year and each state individually. Thus, all the values that have the same `STATE` and `YEAR` values will be summed together, rather than summing using all of the values in the `SUB_POP` variable. The `.groups` argument allows us to remove the grouping after we perform the calculation with `summarize()`.

```{r}
pop_77_79 <- dem_77_79 %>%
  group_by(YEAR, STATE) %>%
  summarize("TOT_POP" = sum(SUB_POP), .groups = "drop") 

pop_77_79 
```


 Now we will add the population value to the demographic tibble using the `left_join()` function of the `dplyr` package. It is important that we specify how this should be done, that the `YEAR` and `STATE` variable values should match each other. This will place the `dem_77_79` variables to the left of the `pop_77_79` data. 
 
```{r}
dem_77_79 <- dem_77_79 %>%
  left_join(pop_77_79, by = c("YEAR","STATE"))

dem_77_79
```

We will also calculate the percentage that each group makes up of the total population, by dividing the `SUB_POP` by the `TOT_POP` and multiplying by 100 using the `mutate()` function. we will also remove the other population variables.

```{r}
dem_77_79 %<>%
  mutate(PERC_SUB_POP = (SUB_POP/TOT_POP)*100) %>%
  select(-SUB_POP, -TOT_POP)

dem_77_79
```
It is important to make sure that we have the total values we would expect. We have two levels of `SEX`, three levels of `Race`, three levels of `YEAR`, eighteen levels of `AGE_GROUP`, and fifty one levels of `STATE`. If we multiply this together we get 16,524 which is the same as the number of rows in our final `dem_77_79` data. Looks good!

Also Let's make the values of the `SEX` variable capitalized so that they match the other values of the other variables like `RACE` etc. This will help us to keep consistent values across the different years as we wrangle the data for the other decades. To do so we will use the `str_to_title()` function of the `stringr` package. We need to use the `pull()` function to get the values of `SEX` out of `dem_77_79`. Once we make them capitalized they are then reassigned to the `SEX` variable. 

```{r}

dem_77_79 %<>%
  mutate(SEX = str_to_title(pull(dem_77_79, SEX)))

# This can also be done line this:
dem_77_79 %<>%
  mutate(SEX = str_to_title(pull(., SEX)))
```

<font size="6"> **1980-1989**</font>

***


For this decade each year is a separate tibble and they are combined as a list.
```{r}
class(dem_80_89)
```

So the first thing we need to do is combine each tibble of the list together. We can do that using the `bind_rows()` function of `dplyr` which appends the data together based on the presence of columns with the same name in the different tibbles. We will use the `map_df()` function of the `purrr` package to allow us to do this across each tibble in our list. 

```{r}
dem_80_89 <- dem_80_89 %>%
  map_df(bind_rows)

glimpse(dem_80_89 )
```

Great! Now our data is all together.

Now we will wrangle the data similarly to the previous decade.
```{r}
dem_80_89 <- dem_80_89 %>%
  rename("race_sex" =`Race/Sex Indicator`) %>%
  mutate(SEX = str_extract(race_sex, "male|female"),
        RACE = str_extract(race_sex, "Black|White|Other"))%>%
  select( -`race_sex`) %>%
  rename("YEAR" = `Year of Estimate`)
         
glimpse(dem_80_89)
```
Notice that this time the state information is based on the numeric FIPS value. We want only the first two values, as the rest indicate the county. We can use the `str_sub()` function of the `stringr` package for this. We will specify that we want to start at the first position and end at the second.  Just like `str_extract()` we need to rename this variable first so that it is compatible. 
```{r}
dem_80_89 %<>%
rename("STATEFP_temp" = "FIPS State and County Codes") %>%
mutate(STATEFP = str_sub(STATEFP_temp, start = 1, end = 2)) %>%
    left_join(STATE_FIPS, by = "STATEFP") %>%
  dplyr::select(-STATEFP)

glimpse(dem_80_89)
```


```{r}
dem_80_89 %<>%
  pivot_longer(cols=contains("years"),
               names_to = "AGE_GROUP",
               values_to = "SUB_POP_temp") %>%
  group_by(YEAR, STATE, AGE_GROUP, SEX, RACE) %>%
  summarize(SUB_POP = sum(SUB_POP_temp), .groups="drop")

dem_80_89
```
  
```{r}
pop_80_89 <- dem_80_89 %>%
  group_by(YEAR, STATE) %>%
  summarize("TOT_POP" = sum(SUB_POP), .groups = "drop") 


dem_80_89 <- dem_80_89 %>%
  left_join(pop_80_89, by = c("YEAR","STATE")) %>%
  mutate(PERC_SUB_POP = (SUB_POP/TOT_POP)*100) %>%
  dplyr::select(-SUB_POP, -TOT_POP)

dem_80_89
```

Just like with the data from the 70s we will also change the values for `SEX` to be capitalized.

```{r}
dem_80_89 %<>%
  mutate(SEX = str_to_title(pull(., SEX)))
```

Again, it is important to make sure that we have the total values we would expect. This time we have: two levels of `SEX`, three levels of `Race`, ten levels of `YEAR`, eighteen levels of `AGE_GROUP`, and fifty one levels of `STATE`.

If we multiply these together we get 55,080, which is the same as the number of rows of the final `dem_80_89` data. Looks good!

<font size="6"> **1990-1999**</font>

***

Just like the 80s we need to combine the data across the files:

```{r}
dem_90_99 <- dem_90_99 %>%
  map_df(bind_rows)
```

```{r}
glimpse(dem_90_99)
```
For this decade the column names can't all be imported in a simple way from the table, so they need to be recoded.

Here is what the data looks like before importing:

```{r, echo = FALSE, out.width = "800 px"}
knitr::include_graphics(here::here("img", "90.png"))
```

So, first using the base `colnames()` function we change the names of the column names.

```{r}

colnames(dem_90_99) <- c("YEAR",
                         "STATEFP",
                         "Age",
                         "NH_W_M",
                         "NH_W_F",
                         "NH_B_M",
                         "NH_B_F",
                         "NH_AIAN_M",
                         "NH_AIAN_F",
                         "NH_API_M",
                         "NH_API_F",
                         "H_W_M",
                         "H_W_F",
                         "H_B_M",
                         "H_B_F",
                         "H_AIAN_M",
                         "H_AIAN_F",
                         "H_API_M",
                         "H_API_F")

glimpse(dem_90_99)
```

Notice also that the first row is all `NA` values from white space in the original table for 1990, this is probably true for each year. We can check them dimensions of our table using the base `dim()` function. When we filter for rows where `YEAR` is `NA`, we indeed see 10 rows, which is what we would expect if we have a row like this for each of the years in the decade. We see the same if we try a different variable. Now we will test to see how large our tibble is if we drop rows with `NA` values using the `drop_na()` function of `tidyr`. We that indeed our dimensions only changed by ten, so there are not other rows with missing values that we might not expect. So now we will resign the `dem_90_99` variable after removing these rows.

```{r}

dim(dem_90_99)

dem_90_99 %>%
  filter(is.na(YEAR))

dem_90_99 %>%
  filter(is.na(Age)) 

dem_90_99 %>%drop_na() 

dem_90_99 %<>%drop_na() 
```

Then we sum across the non-hispanic and Hispanic groups because this information is not available for the other previous decades. Then we will remove the variables for the Hispanic and non-Hispanic subgroups using `select()`.

```{r}

dem_90_99%<>%
    mutate(W_M = NH_W_M + H_W_M,
           W_F = NH_W_F + H_W_F,
           B_M = NH_B_M + H_B_M,
           B_F = NH_B_F + H_B_F,
           AIAN_M = NH_AIAN_M + H_AIAN_M,
           AIAN_F = NH_AIAN_F + H_AIAN_F,
           API_M = NH_API_M + H_API_M,
           API_F = NH_API_F + H_API_F) %>%
  select(-starts_with("NH_"), -starts_with("H_"))

glimpse(dem_90_99)
```

Looking better! We also need to add age groups like the other decades. We will take a look at the 80s data using the `distinct()` function of the `dplyr` package to see what age groups we need. We can use the base `cut()` function to create a new variable with `mutate()` called `AGE_GROUP` that will have a label for every change in 5 years of age. The `right = FALSE` argument specifies that the interval is not closed on the right, meaning that if the value is at the cut point like the `Age` value is 5, then it will be in the `5 to 9 years` group.

We can make the labels for the `AGE_GROUP` variable match those of `dem_77_79` but we need to pull out the values of the tibble created by `distinct()`. To do this we can use the `pull()` function from the `dplyr` package. Note that it is important to check that the `AGE_GROUP` values are listed in order for `dem_77_79`. We will also remove the `Age` variable after we create the new `AGE_GROUP` variable for the `dem_90_99` data. 


```{r}

distinct(dem_77_79, AGE_GROUP)
pull(distinct(dem_77_79, AGE_GROUP))

dem_90_99 %<>%
  mutate(AGE_GROUP = cut(Age,
                         breaks = seq(0,90, by=5),
                         right = FALSE, labels = pull(distinct(dem_77_79,AGE_GROUP), AGE_GROUP))) %>%
  select(-Age)

glimpse(dem_90_99)

```

Like the previous decades we will create a `RACE` and `SUB_POP` variable using `pivot_longer()` to create a single `Race` variable out of all the subgroup variables. 

Now we need to collapse the data for the various races so that it matches the previous decades. This time we will use the `case_when()` function of the `dplyr` package and the `str_detect()` function of the `stringr` package to identify when the race is something other than `B` or `W` and replace with the value `Other`. The value to the right of the `~` indicates what we want the value of the new variable to be if the value of the variable we are using with `str_decect()` matches the condition specified. If the value does not match the specified condition, than the other values will be what ever is listed after `TRUE ~`. We will then create population counts as we did previously for the other decades.

Finally, we will create new sums for the sub-populations where we sum across the two `Other` subgroups `Race`  to a create a single value for each value of `YEAR`, `SEX`, `AGE_GROUP`, and `STATE` by using the `group_by()` function and `summarie()`.  

```{r}
dem_90_99  %<>%
  pivot_longer(cols = c(starts_with("W_"),
                    starts_with("B_"),
                    starts_with("AIAN_"),
                    starts_with("API_")),
               names_to = "RACE",
               values_to = "SUB_POP_temp")

dem_90_99 %<>%
  mutate(SEX = case_when(str_detect(RACE, "_M") ~ "Male",
                         TRUE ~ "Female"),
         RACE = case_when(str_detect(RACE, "W_") ~ "White",
                          str_detect(RACE, "B_") ~ "Black",
                          TRUE ~ "Other")) %>%
  left_join(STATE_FIPS, by = "STATEFP") %>%
  dplyr::select(-STATEFP)

dem_90_99 %<>%
  group_by(YEAR, STATE, AGE_GROUP, SEX, RACE) %>%
  summarize(SUB_POP = sum(SUB_POP_temp), .groups="drop")

```

```{r}
pop_90_99 <- dem_90_99 %>%
  group_by(YEAR, STATE) %>%
  summarize(TOT_POP = sum(SUB_POP), .groups = "drop")

dem_90_99 <- dem_90_99 %>%
  left_join(pop_90_99, by=c("YEAR", "STATE")) %>%
  mutate(PERC_SUB_POP = (SUB_POP/TOT_POP)*100) %>%
  dplyr::select(-SUB_POP, -TOT_POP)

dem_90_99
```


Again, we should check to make sure that we have the total values we would expect. We have the same number of unique values for each of our variables as in with the data from the 80s, so if we collapsed the data for the different additional sub-populations in this data, then we have done it correctly. 

Indeed it looks like we have 55,080 rows, which is what we would expect and is the same as the number of rows of the final `dem_80_89` data. Looks good!

<font size="6"> **2000-2010**</font>

***

Again, for this decade we need to combine the data across years.

```{r}
dem_00_10 <- dem_00_10 %>%
  map_df(bind_rows)

glimpse(dem_00_10)

```

OK, the data looks a bit different from the others. First we will remove a couple of variables that we probably don't need. Also it looks like we have some values for the entire United Sates and we will drop these to be like the other decades.



```{r}
dem_00_10 %<>%
  select(-ESTIMATESBASE2000,-CENSUS2010POP) %>%
  filter(NAME != "United States")
```

We can see that there are lots of values that are zero. According to the [technical documentation](https://www2.census.gov/programs-surveys/popest/technical-documentation/file-layouts/2000-2010/intercensal/state/st-est00int-alldata.pdf){target="_blank"} for this data, zero values indicate the total for the other categories of `Sex`, `Origin`, `Race`, and `AGEGRP`.


```{r, echo = FALSE, out.width = "600 px"}
knitr::include_graphics(here::here("img", "tech_info.png"))
```

So we will drop the total values for `SEX`, `RACE`, and `AGEGRP` by removing the rows where these variables are equal to zero.

We will also want to only select for the total values for `Origin` as we do not wish to divide the data into subgroups about Hispanic ethnicity because we do not have that information for the first two decades. Thus we will filter for only the rows where `Origin` is equal to zero.

We will also then remove the `REGION`, `Division`, `STATE`, and `Origin` variables. We will then rename `NAME` to be `STATE` and rename `AGEGRP` to be like the other decades as `AGE_GROUP`.

```{r}
dem_00_10 %<>%
  filter(SEX != 0,
         RACE != 0,
         AGEGRP != 0, 
         ORIGIN == 0) %>%
  dplyr::select(-REGION, -DIVISION, -ORIGIN, -STATE) %>%
  rename("STATE" = NAME,
         "AGE_GROUP" = AGEGRP)

dem_00_10
```


Now we need to recode the numeric values to the values in the technical documentation. We can do so by adding labels to each numeric level using the base function `factor()`.

```{r}
dem_00_10 %<>%
  mutate(SEX = factor(SEX,
                            levels = 1:2,
                            labels = c("Male",
                                    "Female")),
         RACE = factor(RACE,
                            levels = 1:6,
                            labels = c("White",
                                    "Black",
                                    rep("Other",4))),
         AGE_GROUP = factor(AGE_GROUP,
                            levels = 1:18,
                            labels = pull(distinct(dem_77_79,AGE_GROUP), AGE_GROUP)))
                            
glimpse(dem_00_10)
```

OK, we also want to change the shape of the data so that we have a `YEAR` variable and each estimate of the population is a value in a new variable called `SUB_POP_temp`. 

```{r}
dem_00_10 %<>%
  pivot_longer(cols=contains("ESTIMATE"),
               names_to = "YEAR",
               values_to = "SUB_POP_temp")
```

We will now clean up the `YEAR` variable to only be the numeric value by keeping only the last 4 values of each string using the `str_sub()` function of the `stringr` package.

```{r}
dem_00_10 %<>%
  mutate(YEAR = str_sub(YEAR, start=-4)) %>%
  mutate(YEAR = as.numeric(YEAR))
```


Now we will collapse the data for the different RACES and calculate a new `SUB_POP` value. 

```{r}
dem_00_10 %<>%
  group_by(YEAR, AGE_GROUP, STATE, SEX, RACE) %>%
  summarize(SUB_POP = sum(SUB_POP_temp), .groups = "drop")
```

Again, the dimensions look as we expect with 60,588 rows. This time we have two levels of `SEX`, three levels of `Race`, **11** levels of `YEAR`, eighteen levels of `AGE_GROUP`, and fifty one levels of `STATE`. If we multiply this together we get 16,588. Looks good!

Now we will calculate the total population and percent of the total as we have done with the previous decades.


```{r}
pop_00_10 <- dem_00_10 %>%
  group_by(YEAR, STATE) %>%
  summarize(TOT_POP = sum(SUB_POP), .groups = "drop")
```

We can also check that our wrangling was performed correctly by summing the values for the individual sub-populations percentages and seeing if it totals to 100.

```{r}
dem_00_10 %>%
  left_join(pop_00_10, by=c("YEAR", "STATE")) %>%
  group_by(YEAR, STATE) %>%
  mutate(PERC_SUB_POP = (SUB_POP/TOT_POP)*100) %>%
  summarize(perc_tot = sum(PERC_SUB_POP), .groups = "drop") %>%
  mutate(poss_error = case_when(abs(perc_tot - 100) > 0 ~ TRUE,
                                TRUE ~ FALSE)) %>%
  group_by(poss_error) %>%
  tally()

```

Looks like the percentages for each state for each year all add up to 100, as we would expect. Great! Now we will reassign the `dem_00_10` data with this processing. 

```{r}
dem_00_10 %<>%
  left_join(pop_00_10, by = c("YEAR", "STATE")) %>%
  mutate(PERC_SUB_POP = (SUB_POP/TOT_POP)*100) %>%
 select(-SUB_POP, -TOT_POP)

dem_00_10
```

OK, now we are ready to combine all of our demographic data together!

<!-- </details> -->

## **Combining demographic data**
***

We can check that the column names are the same for the data for each of the decades by using the `setequal()` function of the `dplyr` package.

```{r}
setequal(colnames(dem_77_79),colnames(dem_80_89))
setequal(colnames(dem_80_89),colnames(dem_90_99))
setequal(colnames(dem_90_99),colnames(dem_00_10))
```


We can also confirm that we have the same number of age groups for each decade by using the base `length()` function. If you did not take a look at the wrangling for the demographic data then you may be unfamiliar with the `pull()` function of the `dplyr` package. This allows you to grab the values of a variable from a tibble. The `distinct()` function which is also of the `dplyr` package creates a tibble of the unique values for a variable.

```{r}
length(pull(distinct(dem_77_79, AGE_GROUP), AGE_GROUP))
length(pull(distinct(dem_80_89, AGE_GROUP), AGE_GROUP))
length(pull(distinct(dem_90_99, AGE_GROUP), AGE_GROUP))
length(pull(distinct(dem_00_10, AGE_GROUP), AGE_GROUP))
```

Looks good!


Now we will combine the data using the `bind_rows()` function of the `dplyr` package. This function appends the data together based on the presence of columns with the same name in the different tibbles.

```{r}
dem <- bind_rows(dem_77_79,
                 dem_80_89,
                 dem_90_99,
                 dem_00_10)
```


```{r}
glimpse(dem)
```

Great! now we have a really large single tibble.

Now we want to select similar demographic data to what was used in the previous analyses.

Here is the table from the [Donohue paper](https://www.nber.org/papers/w23510.pdf){target="_blank"} that compares the data used in the analyses.


```{r, echo=FALSE, out.height = '100%', out.width = '100%', fig.align='center'}
knitr::include_graphics(here("img",'Donohue_Table2.png'))
```
We can see that only the percentage of males that were from age 15-39 of the race groups (black, white, and other) were used in the Donohue analysis.

Ultimately we intend to make a tibble of data that is similar to each analysis. Therefore, we will create a data tibble about the demographic data for each analysis now.

To do so we will first create a vector of the age groups that should be included in the Donohue-like analysis, that we will call `DONOHUE_AGE_GROUPS`. We will then filter for only the age groups in this vector by using the `filter()` function of the `dplyr` package and the `%in%` operator to indicate that we want to keep all `AGE_GROUP` values that are equal to those within `DONOHUE_AGE_GROUPS`. We also want to filter for only population percentages for males by using the `==` operator. Then we can collapse the age groups from 20-39 by using the `fct_collpase()` function of the `forcats` package.

```{r}
DONOHUE_AGE_GROUPS <- c("15 to 19 years",
                        "20 to 24 years",
                        "25 to 29 years",
                        "30 to 34 years",
                        "35 to 39 years")

dem_DONOHUE <- dem %>%
  filter(AGE_GROUP %in% DONOHUE_AGE_GROUPS,
               SEX == "Male") %>%
  mutate(AGE_GROUP = fct_collapse(AGE_GROUP, "20 to 39 years"=c("20 to 24 years",
                                                                "25 to 29 years",
                                                                "30 to 34 years",
                                                                "35 to 39 years")))

dem_DONOHUE
```

We also want to create a new variable that will contain all the demographic information for each percentage just as was done in the [Donohue, et al.](https://www.nber.org/papers/w23510.pdf){target="_blank"} analysis. This should result in 6 different demographic variables.

To do this we will modify the `AGE_GROUP` variable by using the `mutate()` function of the `dplyr` package. We will replace the spaces in the now two age group categories with and underscore using the `str_replace_all()` function of the `stringr` package which replaces all instances of a pattern in a character string. 

Then we will use the `group_by()` function and the `summarize()` function also of the `dplyr` package to allow us to calculate a sum of the percentages for each of the sub-population percentages for the newly modified age groups in `AGE_GROUP`. The `.groups = "drop"` argument allows for the grouping to be removed after the `summarize()` function.

```{r}
dem_DONOHUE %<>%
  mutate(AGE_GROUP = str_replace_all(string = AGE_GROUP, 
                                     pattern = " ", 
                                     replacement = "_")) %>%
  group_by(YEAR, STATE, RACE, SEX, AGE_GROUP) %>%
  summarize(PERC_SUB_POP = sum(PERC_SUB_POP), .groups = "drop")

dem_DONOHUE
```

Now we will combine the variables `RACE`, `SEX`, and `AGE_GROUP` together into one string separated by underscores using the `unite` function of the `tidyr` package. we will call this new variable `VARIABLE`.
We will rename the `PERC_SUB_POP` variable to be `VALUE` using the `rename()` function of the `dplyr` package. The new name should be listed first before the `=`.

```{r}
dem_DONOHUE %<>%
  unite(col = "VARIABLE", RACE, SEX, AGE_GROUP, sep = "_") %>%
  rename("VALUE" = PERC_SUB_POP)

dem_DONOHUE
```

Let's do a quick row number check. We have six different demographic variables, 51 states (DC counts as a state in this case), and 34 different years from 1977 to 2010, we should have 10,404 rows, which we do!

Now, let's do the same for the "Lott-like" analysis.


```{r, echo=FALSE, out.height = '100%', out.width = '100%', fig.align='center'}
knitr::include_graphics(here("img",'Donohue_Table2.png'))
```

So, in this analysis there were 36 variables covering percentages of individuals from 10 to over 65, three  race groups and both males and females. This table is misprinted and does not include the word "Other" for the third race group that was used. 

First we will filter out the age groups that were not included. Then we will collapse the age groups to those that were used by Mustard and Lott again using the `fct_collpase()` function of the `forcats` package. 

Also we will again combine the values across the variables to create a new demographic variable with 36 levels. 

```{r}
LOTT_AGE_GROUPS_NULL <- c("Under 5 years",
                          "5 to 9 years")

dem_LOTT <- dem %>%
  filter(!(AGE_GROUP %in% LOTT_AGE_GROUPS_NULL) )%>%
  mutate(AGE_GROUP = fct_collapse(AGE_GROUP,
                                  "10 to 19 years"=c("10 to 14 years",
                                                     "15 to 19 years"),
                                  "20 to 29 years"=c("20 to 24 years",
                                                     "25 to 29 years"),
                                  "30 to 39 years"=c("30 to 34 years",
                                                     "35 to 39 years"),
                                  "40 to 49 years"=c("40 to 44 years",
                                                     "45 to 49 years"),
                                  "50 to 64 years"=c("50 to 54 years",
                                                     "55 to 59 years",
                                                     "60 to 64 years"),
                                  "65 years and over"=c("65 to 69 years",
                                                        "70 to 74 years",
                                                        "75 to 79 years",
                                                        "80 to 84 years",
                                                        "85 years and over"))) %>%
  mutate(AGE_GROUP = str_replace_all(AGE_GROUP," ","_")) %>%
  group_by(YEAR, STATE, RACE, SEX, AGE_GROUP) %>%
  summarize(PERC_SUB_POP = sum(PERC_SUB_POP), .groups = "drop") %>%
  unite(col = "VARIABLE", RACE, SEX, AGE_GROUP, sep = "_") %>%
  rename("VALUE"=PERC_SUB_POP)
```

We can indeed check that we have the correct number of levels for `VARIABLE` using the `distinct()` function.

```{r}
 distinct(dem_LOTT, VARIABLE)
```
  
## **Combining population data**
***

We also have population data for each decade that came from wrangling the demographic data.

We again want to combine this data, so let's again make sure that all the different tibbles have the same column names.

```{r}
setequal(colnames(pop_77_79),colnames(pop_80_89))
setequal(colnames(pop_80_89),colnames(pop_90_99))
setequal(colnames(pop_90_99),colnames(pop_00_10))

head(pop_77_79)
head(pop_80_89)
head(pop_90_99)
head(pop_00_10)
```

Looks good!

```{r}
population_data <- bind_rows(pop_77_79,
                             pop_80_89,
                             pop_90_99,
                             pop_00_10)

population_data <- population_data %>%
  mutate(VARIABLE = "Population") %>%
  rename("VALUE" = TOT_POP)
```

We could check that we have 51 values for each year by using the `count()` function of the `dplyr` package.

```{r}
population_data %>%
  count(YEAR)
```

## **Police staffing**
***

<details><summary> Click here to see details about how the police staffing data was wrangled. </summary>

OK, now we will wrangle the police staffing data. We want to limit the data to only the years of interest. Then we will also replace NA values with zero for the `male_total_ct` and `female_total_ct` variables using the `replace_na()` function of the `tidyr` package. We will also, use the `across()` function of the `dplyr` package to select and mutate both of these columns in this way. Since both of these variables have `total_ct` in the name and no other variables do, we can use the `contains()` function of the `dplyr` package to specify that we want to use these columns instead of listing both out.


```{r}
glimpse(ps_data)

ps_data %<>%
  filter(data_year >= 1977, 
         data_year <= 2014) %>%
mutate(across(.cols =contains("total_ct"), ~replace_na(., 0)))

glimpse(ps_data)
```

Now we can create a new variable called `officer_total` which will be the sum of these variables. We will then keep just this variable as well as the `data_year`, `pub_agency_name`, and `state_abbr`.

```{r}

ps_data %<>%
  mutate(officer_total = male_total_ct + female_total_ct) %>%
  dplyr::select(data_year,
                pub_agency_name,
                state_abbr,
                officer_total)

ps_data
```

Now we also want to get collapse by `pub_agency_name` to get a total count for each year and each state. So we will do this by using the `group_by()` function and grouping by `data_year` and `state_abbr` and using the `summarize()` function to calculate a sum.

```{r}
ps_data %<>%
  group_by(data_year, state_abbr) %>%
  summarize(officer_state_total=sum(officer_total), .groups = "drop")

ps_data
```
And we will check that we have same number of values (the number of years included in the data) for each state.

```{r}

ps_data %>%
  count(state_abbr)  %>% head()

ps_data %>%
  count(state_abbr) %>%
  filter(n != 38) %>%
  dim()
```
Looks like all the states have 38 values.

Notice also that there are some unusual abbreviations in the `state_abbr` variable.

We will remove data for [US terroitories and associated states]( https://www.fs.fed.us/database/feis/format.html){target="_blank"}  


Abbreviation   | Territory and associated states                                                                    
---------- |-------------
**AS**  | American Samoa 
**GM**  | Guam
**CZ**  | Canal Zone
**FS**  | ??Federated States of Micronesia (usually FM)  
**MP**  |  Northern Mariana Islands
**OT**  | ??U.S. Minor Outlying Islands (usually UM)
**PR**  | Puerto Rico 
**VI**  | Virgin Islands


```{r}

state_of_interest_NULL <- c("AS",
                            "GM",
                            "CZ",
                            "FS",
                            "MP",
                            "OT",
                            "PR",
                            "VI")

ps_data <- ps_data %>%
  filter(!(state_abbr %in% state_of_interest_NULL)) 
```
  
Within the `datasets` package that is loaded with R, there is a data set called `state` that contains an object called `state.abb` that has the state abbreviations and `state.name` that has the state names.
We will combine these now to add the state names to our data.

```{r}
state_abb_data <- tibble( "state_abbr" = state.abb, "STATE" =state.name)
head(state_abb_data)
```

One unusual thing about this data is that NE is used for Nebraska to avoid confusions with NB in Canada. 
So we want to replace that using the `str_replace()` function of the `stringr` package

```{r}
state_abb_data %<>%
  mutate(state_abbr = str_replace(string = state_abbr, 
                                pattern = "NE", 
                            replacement = "NB"))
```

#### {.scrollable }
```{r}
# Scroll through the output!
state_abb_data %>% print(n = 50)
```
####


We need to add DC to this. We will use the `add_row()` function of `dplyr` to do this.  We just need to specify values for both of the variables.

```{r}
state_abb_data %<>%
  dplyr::add_row(state_abbr = "DC",
                      STATE = "District of Columbia")
```

Now we will add this to our police staffing data and then remove the `state_abbr` variable, so that we just have state names. We will also 

```{r}
ps_data %<>%
  left_join(state_abb_data, by = "state_abbr") %>%
  dplyr::select(-state_abbr)
ps_data
```

Now we will rename the variables to match those of the other datasets.
```{r}
ps_data %<>%
  rename(YEAR = "data_year",
         VALUE = "officer_state_total") %>%
  mutate(VARIABLE = "officer_state_total")
ps_data
```


We also need to adjust the value to be that of every 100,000 people in the state. To do so we need the population for each state, which luckily we already have. We will slightly modify the population data and create a new tibble that will make it more clear how we are dividing by it.

```{r}
denominator_temp <- population_data %>%
 select(-VARIABLE) %>%
  rename("Population_temp"=VALUE)
head(denominator_temp)

ps_data %<>%
  left_join(denominator_temp, by=c("STATE","YEAR"))
head(ps_data)
```



```{r}
ps_data %<>%
  mutate(VALUE = (VALUE * 100000) / Population_temp) %>%
  #mutate(VALUE = lag(VALUE)) %>%
  mutate(VARIABLE = "police_per_100k_lag") %>%
  select(-Population_temp)

ps_data
```

<!-- </details> -->



## **Unemployment**
***

The first thing we need to do with the unemployment data is combine the data across the different states.
 We can do that using the `bind_rows()` function of `dplyr` which appends the data together based on the presence of columns with the same name in the different tibbles. We will use the `map_df()` function of the `purrr` package to allow us to do this across each tibble in our list. We will then select just the annual data for each state and year and we will rename our variables to be consistent with some of other data that we are working with. Thus we would like our variables to be `YEAR`, `VALUE` and `VARIABLE` in all caps.
 
```{r}

ue_rate_data <- ue_rate_data %>%
  map_df(bind_rows, .id = "STATE")

head(ue_rate_data)

ue_rate_data <- ue_rate_data %>%
  dplyr::select(STATE, Year, Annual) %>%
  rename("YEAR" = Year,
        "VALUE" = Annual) %>%
  mutate(VARIABLE = "Unemployment_rate")

head(ue_rate_data)
```

## **Poverty rate**
***

<details><summary> Click here to see details about how the poverty data was wrangled </summary>

OK, now for wrangling the poverty data. First let's take a look at it. 
```{r}
head(poverty_rate_data)
```

We can see that the column names are actually shifted down below the row with the year. So we will manually make these values the actual column names.

```{r}
colnames(poverty_rate_data) <- c("STATE",
                                 "Total",
                                 "Number",
                                 "Number_se",
                                 "Percent",
                                 "Percent_se")

poverty_rate_data2 <-poverty_rate_data

```

Let's also remove the rows where the column names are listed, like row number 2.

```{r}
poverty_rate_data  %<>%
  filter(STATE != "STATE")
head(poverty_rate_data)
```

We can also see that there are some extra notes at the end of our data. This is why it is a good idea to look at both the head and tail of your data.

```{r}
tail(poverty_rate_data)
```
We can see that the strings for the state for these rows are very long. We can also see that there are rows that just have the year, where the state is only 4 characters long. We will create a new variable called `length_state` based on the number of characters in the `STATE` values. We will use the `str_length()` function of the `stringr` package. We need to use the `map_dbl()` function to apply this to each row of the `STATE` variable. The `map()` function creates a list, whereas the `map_dbl()` function creates a vector of class double. If we were to use `map()` we would need to use `unlist()` and `pull()`.

```{r}

poverty_rate_data %<>%
 mutate(length_state = map_dbl(STATE, str_length))

# Alternatively with map()
#poverty_rate_data %<>%
#mutate(length_state = unlist(map(pull(poverty_rate_data, STATE), str_length)))

poverty_rate_data
```


Great, now let's look at the tail with our new variable `length_state`
```{r}
tail(pull(poverty_rate_data, length_state))

```

```{r}
poverty_rate_data %<>% 
  filter(length_state <100)

tail(poverty_rate_data)
```
Looks good!

Now let's select all the states that are actually year values to create a new variable about the year. We can do so by using the `str_detect()` function of the `stringr` package to look for digits or values of 0-9. This is indicated by using the `"[:digit:]"`.

As you can see in the [RStudio cheatsheet](https://rstudio.com/resources/cheatsheets/){target="_blank"}  about regular expressions this notation indicates any digit between 0 and 9.

```{r}
knitr::include_graphics(here("img", "regex.png"))
```

#### {.scrollable }

```{r}
# Scroll through the output!
poverty_rate_data %>% 
  filter(str_detect(STATE, "[:digit:]")) %>%
  print(n = 51)
```

####

Some of the years (2013 and 2017) are listed twice with a number in parentheses, others are just listed once with a number in parentheses. Looking at the technical documentation, this seems to do with updates to the definition of poverty and to the methods used to estimate poverty levels. See [here](https://www.census.gov/topics/income-poverty/poverty/guidance/poverty-footnotes/cps-historic-footnotes.html){target="_blank"} and [here](https://www2.census.gov/programs-surveys/cps/techdocs/cpsmar19.pdf){target="_blank"} for more information. We will simply select one of the sets of data for 2013 and 2017.

```{r}
poverty_rate_data %>% 
  filter(str_detect(STATE, "2013")) %>%
  filter(str_detect(STATE, "2017"))
```

First let's add the year value to our data. 


There should be consistently data for 51 states (including DC). We can see that sometimes DC is spelled out and sometimes it is not.

#### {.scrollable }
```{r}
### Scroll through the output!
poverty_rate_data %>% 
  filter(str_detect(STATE, "[:alpha:]")) %>%
  distinct(STATE) %>% print(n = 100)

```
####


Now we will replace `"D.C."` with `"District of Columbia"` using `str_replace()`. We can use the `tally()` function of the `dplyr` package to check that we have fewer now.

```{r}
poverty_rate_data %<>% 
mutate(STATE = str_replace(STATE, pattern = "D.C.", 
                              replacement = "District of Columbia" ))

poverty_rate_data %>% 
  filter(str_detect(STATE, "[:alpha:]")) %>%
  distinct(STATE) %>% tally()
```
Great! Now are each of the states occurring as often as the unique year values? We can first check how many year values there are. Then can use the `count()` function of the `dplyr` package to check how often the states are repeated.

```{r}

poverty_rate_data %>% 
  filter(str_detect(STATE, "[:digit:]")) %>%
  tally()
```

There are 41 different sets of data according to year values.

#### {.scrollable }
```{r}
### Scroll through the output!
poverty_rate_data %>% 
  filter(str_detect(STATE, "[:alpha:]")) %>%
  count(STATE) %>% 
  print(n = 51)
```
####

Indeed, looks like each of the states are repeated the same number of times!

Now let's create a new variable `YEAR` that repeats the year values for all of the different states and for the row that has just the year value for a total of 52.

```{r}

year_values <- poverty_rate_data %>% 
  filter(str_detect(STATE, "[:digit:]")) %>%
  distinct(STATE)

  year_values<-rep(pull(year_values, STATE), each = 52)
setequal(length(year_values), length(poverty_rate_data$STATE))
```

Now we will add this to our `poverty_rate_data`. We will also remove the `length_state` variable using the `select()` function of the `dplyr` package and a minus sign before the variable name.

```{r}
poverty_rate_data %<>%
  mutate(year_value = year_values) %>%
  select(-length_state)
```

#### {.scrollable }
```{r}
#Scroll through the output!
poverty_rate_data %>% print(n = 100)
```
####

Looks good! Now we will remove the rows that have just the year values by only preserving those with alpha characters.

```{r}
poverty_rate_data %<>%
    filter(str_detect(STATE, "[:alpha:]"))

```

Now let's remove the older data for 2013 and 2017 which is the data that appears lower in the tibble.

```{r}
poverty_rate_data %<>%
filter(year_value != "2017") %>%
filter(year_value != "2013 (18)")
```


We also want to just keep the first 4 digits of the year_value and create a `YEAR` variable. We need to pull the `year_value` data because `str_sub()` expects a character vector not a tibble.

```{r}
poverty_rate_data %<>%
  mutate(YEAR = str_sub(pull(., year_value), start = 1, end=4))
```

#### {.scrollable }
```{r}
poverty_rate_data 
```
####

Looks good! Now we will just remove the extra variables and rename the variables we want to keep to be similar to our other data.

```{r}
poverty_rate_data %<>%
  dplyr::select(- Number,
                - Number_se,
                - Percent_se,
                - Total,
                - year_value) %>%
  rename("VALUE" = Percent) %>%
  mutate(VARIABLE = "Poverty_rate",
         YEAR = as.numeric(YEAR),
         VALUE = as.numeric(VALUE))
head(poverty_rate_data)
```

Looks great! 


<!-- </details> -->



## **Violent crime**
***

<details><summary> Click here to see details about how the violent crime data was wrangled </summary>

The `crime_data` was imported using `read_lines()` and we have some lines that we don't necessarily need. A large amount of the original data is notes at the end of the table. We want to remove these lines. We can determine where they start by searching for the row that contains the first statement of these notes using the `str_which()` function of the `stringr` package. We will subtract one from this as there is a blank line in between.

```{r}
tail(crime_data)
crime_data <- crime_data[-((str_which(crime_data, "The figures shown in this column for the offense of rape were estimated using the legacy UCR definition of rape")-1): length(crime_data))]
#crime_data <- crime_data[-(2143:length(crime_data))]
tail(crime_data)
```

There are lines for each year from 1977 to 2014 as well as four lines about each state and the header information for each state. 
Here you can see what the original data looks like:
```{r}
knitr::include_graphics(here("img", "crime_data.png"))
```

We want to delete the header information and only retain the lines numeric values or state names.  Thus since there are 38 years worth of data for each state and 4 lines for each header, then each state has 42 lines. We want to delete the lines between and including line 2 to 4 for each state. We will save the header information once to use later.
```{r}
head(crime_data)
x <- 2014-1977+1
rep_cycle <- 4 + x
rep_cycle_cut <- 2 + x
colnames_crime<-(crime_data[4])
```

So starting at line 2 and and 3 and 4 we create a sequence of numbers that increase by the number of rows of the length of the individual state data. We can do so using the base `seq()` function. We can take a look at these in order using the base `sort()` function.

```{r}
delete_rows <- c(seq(from = 2,
                       to = length(crime_data),
                       by = rep_cycle),
                 seq(from = 3,
                       to = length(crime_data),
                       by = rep_cycle), 
                 seq(from = 4,
                       to = length(crime_data),
                       by = rep_cycle))
sort(delete_rows)

```
Thus we will delete lines 2, 3, and 4 and then skip 40 lines (to account for the state information for the first state, the lines of information for the 38 years, and then the state information for the next state) and then delete the next 3 consecutive lines and so on. We can indeed see that line 44-46 are what we wish to remove.

```{r}
crime_data[44:46]
```

```{r}
crime_data <- crime_data[-delete_rows]
```


Nice!

Now we can select all the lines that have state information. We can repeat each of these for the 38 years for each state as well as this line that contains the state information by using the base `rep()` function with the `each =` argument. Finally we will remove the `"Estimated crime in "` portion of the string using the `str_remove()` function of the `stringr` package.  We will later combine this with the crime data.

```{r}
state_label_order <-crime_data[str_which(crime_data, "Estimated crime")]
state_label_order

state_label_order <- rep(state_label_order, each = 38)

crime_states <-str_remove(state_label_order, pattern = "Estimated crime in ")
head(crime_states)
```

Nice! Now for the rest of the data. We now need to remove the lines with the state information.

```{r}
crime_data <-crime_data[-str_which(crime_data, "Estimated crime")]
head(crime_data)
tail(crime_data)
```

It appears that the data is comma separated with 8 columns. One of the middle columns often has no values, we need to fill these in with NAs. We can use the `read_csv()` function from the `readr` package to do this. It turns out you don't have to have a file, but you can also use a string our a vector.

```{r}
crime_data_sep <-read_csv(crime_data, col_names = FALSE)
head(crime_data)
```
Nice! Now we just need our column names. Recall that we saved this information. 

```{r}
colnames_crime

colnames(crime_data_sep) <-c("Year",
                             "Population",
                             "Violent_crime_total",
                             "Murder_and_nonnegligent_Manslaughter",
                             "Legacy_rape" ,
                             "Revised_rape", 
                             "Robbery",
                             "Aggravated_assault")
head(crime_data_sep)
```

We also want to combine this with the state information we collected earlier.
We will use the `bind_cols()` function of the `dplyr` package to do this. This requires that the data have the same number of rows.

```{r}
crime_data_sep <-bind_cols(STATE =crime_states, 
          crime_data_sep)

```

Now we will rename the `Viol_crime_count` variable to be `Variable` and we will remove all of the other columns except for `Year`. We will also rename the variables to look like the other datasets.

```{r}
crime_data <- crime_data_sep %>%
  mutate(VARIABLE = "Viol_crime_count") %>%
  rename("VALUE" = Violent_crime_total) %>%
  rename("YEAR" = Year) %>%
  select(YEAR,STATE, VARIABLE, VALUE)

crime_data
```




from Michael:

```{r, eval = FALSE}
crime_data2 <- data.frame(cbind(crime_data2, rep(1:(length(crime_data2)/rep_cycle_cut),each=rep_cycle_cut)))

colnames(crime_data) <- c("String","STATE_GROUP")

crime_data <- crime_data %>%
  group_by(STATE_GROUP) %>%
  group_split()

columns_crime_data <- 8

crime_data <- crime_data %>%
  map(~mutate(.,
               State = case_when(str_detect(String, "Estimated crime in ") ~ substring(String, nchar("Estimated crime in ")+1)),
              row_id = row_number())) %>%
  map(~fill(., State)) %>%
  map(~filter(.,row_id > 2)) %>%
  map(~mutate(.,
              String = paste0(String, ",", State))) %>%
  map(~dplyr::select(.,String)) %>%
  map(~str_split_fixed(.$String,",",columns_crime_data + 1)) %>%
  map(~data.frame(.)) %>%
  map(~rename(.,"YEAR"=X1,
              "Extra_col1"=X2,
              "VC"=X3,
              "Extra_col2"=X4,
              "Extra_col3"=X5,
              "Extra_col4"=X6,
              "Extra_col5"=X7,
              "Extra_col6"=X8,
              "STATE"=X9)) %>%
  map(~dplyr::select(.,-contains("Extra_col"))) %>%
  map(~.x %>% mutate_all(~trimws(.,which = "both"))) %>%
  map_df(bind_rows)

sapply(crime_data, class)

crime_data <- crime_data %>%
  mutate(VARIABLE = "Viol_crime_count") %>%
  rename("VALUE" = VC) %>%
  as.tibble() %>%
  mutate(YEAR = as.numeric(YEAR),
         VALUE = as.numeric(VALUE))
```

<!-- </details> -->

## **RTC laws**
***

<details><summary> Click here to see details about how the RTC Law data was wrangled </summary>


The information about the laws for each state are located on page 62 of the [Donohue, et al.](https://www.nber.org/papers/w23510.pdf){target="_blank"} article, so first we will select just this page. We can print part of the character string for this page using the `utils` `str()` function and the `ncar.max` argument.

```{r}
DAWpaper_p_62 <- DAWpaper[[62]]
str(DAWpaper_p_62, nchar.max = 1000)
```

 We can also use the `cat` function to see the data printed nicely to see what we are going for.
```{r}

cat(DAWpaper_p_62)
```


We can see that this is one continuous character string. We can separate into lines based on the presence of `"\n"` in the string using the `str_split()` function of the `stringr` package. We need to unlist the data first, as the output of `str_split()` is a list. Finally, we can convert it to a tibble using the `as.tibble()` function of the `tibble` package.  We also see that we don't need the first line about the table. We can remove this with the `slice()` function of the `dplyr` package. We can also use this to remove the column names so that we can replace them. Thus we will use `slice(-(1:2))` to remove the first two lines.

So we will split and unlit() the data.
```{r}
p_62 <- DAWpaper_p_62 %>%
    str_split("\n") %>%
    unlist() %>%
    dplyr::as_tibble() %>%
    slice(-(1:2))

head(p_62)
tail(p_62)

```



We also see by looking at the tail that we want to remove the last two lines. One is empty and the other has only 63 characters, which is the line with the page number.

```{r}
p_62 %<>%
  rename(RTC = value)
p_62 %>%
  mutate(RTC = map_chr(RTC, str_length)) %>%tail()

p_62[53,] # physcial page 60
p_62[54,] # empty line
p_62 %<>%
    slice(-c(53:54))
```

Now we will try splitting by spaces. We can show the output withe the `first()` and `nth()` functions of the `dplyr` package.

```{r}
p_62 %>% pull(RTC) %>% map(str_split, pattern = " ") %>% first()
p_62 %>% pull(RTC) %>% map(str_split, pattern = " ") %>% nth( 5)
```


Interesting, we can see that there are lots of spaces between the elements of the table and that they vary by line. For example there are 6 spaces before Alabama and 7 spaces before Alaska.

Overall, that didn't work quite like we expected. 

Recall from the cheatsheet that `"\\s"` indicates a space. There are also ways to specify how many spaces using curly brackets`{}`. 

```{r}
knitr::include_graphics(here("img", "regex.png"))
knitr::include_graphics(here("img", "quantifiers.png"))
```

The spacing appears to vary quite a bit. WE can use the `str_count()` function of the `stringr` package to see how often we have white spaces larger than 5, 10, 15, or 40 spaces.
```{r}
# how often are there white spaces with more than 5 spaces
p_62 %>% 
  pull(RTC) %>% 
  map(str_count, pattern = "\\s{5,}") %>% 
  unlist()
# how often are there white spaces with more than 10 spaces
p_62 %>% 
  pull(RTC) %>% 
  map(str_count, pattern = "\\s{10,}") %>%
  unlist()

# how often are there white spaces with more than 15 spaces
p_62 %>% 
  pull(RTC) %>% 
  map(str_count, pattern = "\\s{15,}") %>%
  unlist()

# how often are there white spaces with more than 40 spaces
p_62 %>% 
  pull(RTC) %>%
  map(str_count, pattern = "\\s{40,}") %>% 
  unlist()
```

Rows with white spaces with more than 40 consecutive spaces is less common. It appears to be the case in the 1st and 5th row. 

If we take a look at those rows we can see that this occurs when we have a missing value.


```{r}
cat(DAWpaper_p_62)
```


So we will replace white spaces with more than 40 consecutive spaces with `NA`. Let's also remove the leading white spaces that varies in front of the state names, as DC does not have any and this could cause a problem later. We will also replace any white spaces of 2 consecutive spaces or more , but less than 15 white spaces with "|" so that we can split the data based on this symbol. Thus we will also put these around the `NA` value that we are using replace the white spaces made of 40+ spaces. 
```{r}

p_62b <-p_62 %>%
  mutate(RTC = str_replace_all(pull(., RTC), "\\s{40,}", "|N/A|")) %>%
  mutate(RTC =str_trim(pull(., RTC), side = "left")) %>%
  mutate(RTC = str_replace_all(pull(., RTC), "\\s{2,15}", "|"))
head(p_62b)
```

Now anytime there is  one or more `"|"` we should have a column break. So now we will split the data by this symbol.
```{r}

p_62b <-pull(p_62b, RTC) %>%
  str_split( "\\|{1,}") 

head(p_62b)
```

Great! Now we want to put our data in tibble format. To do so we need to bind the rows together. We can do so using the base `rbind()` function. We will use this instead of the `bind_rows()` function of `dplyr` because `rbind()` is less restrictive and allows for columns without names. We will use the base `do.call()` function, so that this is performed along each character string within the list of `p_62b` while maintaining the structure. Then we create a tibble out of this. 

```{r}
p_62 <- as.tibble(do.call(rbind, p_62b))

colnames(p_62) <- c("STATE",
                    "E_Date_RTC",
                    "Frac_Yr_Eff_Yr_Pass",
                    "RTC_Date_SA")

p_62 <- p_62 %>%
  dplyr::select(STATE, RTC_Date_SA) %>%
  rename("RTC_LAW_YEAR"= RTC_Date_SA) %>%
  mutate(RTC_LAW_YEAR = as.numeric(RTC_LAW_YEAR)) %>%
  mutate(RTC_LAW_YEAR = case_when(RTC_LAW_YEAR == 0 ~ Inf,
                              TRUE ~ RTC_LAW_YEAR))

RTC <-p_62
RTC
```

## **Joining Data**
***

Now we will join the data from the different data sets together to create a tibble of data for an analysis that will be similar to the data used by [Donohue et al.](https://www.nber.org/papers/w23510.pdf) {target="_blank"} and [Mustard and Lott](https://chicagounbound.uchicago.edu/cgi/viewcontent.cgi?article=1150&context=law_and_economics){target="_blank"}.

First we need to check that our data is indeed ready to be joined. We need to make sure that the column names are the same for each dataset that we intend to combine together. 

We will use the `compare_df_cols()` and `compare_df_cols_same()` functions of the janitor package, to ensure that the column names are the same and that the column values are the same type so that the tibbles can be joined by row. 

If they can be joined by row, then `compare_df_cols_same()`  returns the value `TRUE`, while compare_df_cols(), provides a description of the columns.

```{r}
library(janitor)

data_list <-  list(dem_DONOHUE,
                dem_LOTT,
                population_data,
                ue_rate_data,
                poverty_rate_data,
                crime_data,
                ps_data) #police staffing

janitor::compare_df_cols_same(data_list)
janitor::compare_df_cols(data_list)


checkstate <- function(x) { x %<>% distinct(STATE) %>% tally() %>% pull(n) }
map(data_list, checkstate)
checkyear <- function(x) { x %<>% distinct(YEAR) %>% tally() %>% pull(n) }
map(data_list, checkyear)
```


## **Donohue, et al.**
***

We will now bind the demographic data that we made for the Donohue-like analysis called `dem_DONOHUE`, as well as all the other datasets that we have wrangled. This is possible because we have the same column names for each dataset. We will also use the `pivot_wider()` function of the `tidyr` package to change the shape of the data. This will make the data have more columns. Each unique value in the column called `VARIABLE` will be used to make new columns. and the values for each will come from the column called `VALUE`.

```{r}
DONOHUE_DF <- bind_rows(dem_DONOHUE,
                        ue_rate_data,
                        poverty_rate_data,
                        crime_data,
                        population_data,
                        ps_data)
head(DONOHUE_DF)
```


```{r}
DONOHUE_DF %<>%
  pivot_wider(names_from = "VARIABLE",
              values_from = "VALUE")

DONOHUE_DF %>%
  slice_sample(n = 10) %>%
  glimpse()
```

We will also add the Right to Carry Law data using the `left_join()` function of the `dplyr` package. Which will place the `DONOHUE_DF` data on the left of the `RTC` data.  Values will be matched by STATE. Then we will create a new variable called `RTC_LAW` using the `mutate()` function and the `case_when()` function of the `dplyr` package  that will have the value `TRUE` if the current year data is equal to or greater than the year that a more permissive RTC law was adopted, otherwise the value will be `FALSE`.

```{r}

head(RTC)

DONOHUE_DF %<>%
  left_join(RTC , by = c("STATE")) %>%
  mutate(RTC_LAW = case_when(YEAR >= RTC_LAW_YEAR ~ TRUE,
                              TRUE ~ FALSE))

DONOHUE_DF %>%
  slice_sample(n = 10) %>%
  glimpse()
```

Since we have differing numbers of years for each data set, we can use the `drop_na()` function of the `tidyr` package. to remove years that have incomplete data. Thus any row with NA values will be removed.

For example, we can see that for 1977, although we have most of the data, we do not have the poverty rate. 
```{r}
DONOHUE_DF %>%
  filter(YEAR == 1977) %>%
  head() %>%
  glimpse()

```

Another example, in 2018 we only have information about unemployment rates, poverty rates, and RTC laws.

```{r}
DONOHUE_DF %>%
  filter(YEAR == 2018) %>%
  head() %>%
  glimpse()

```

```{r}
DONOHUE_DF %<>%
 drop_na()

head(DONOHUE_DF) %>% 
  glimpse()
tail(DONOHUE_DF) %>% 
  glimpse()

```

Now we have complete data and the data spans from 1980 to 2010.

```{r}
DONOHUE_DF %>% distinct(YEAR) %>% pull(YEAR)
```

If we include states that had a RTC law adopted before our time span of data, say 1975, then we only have information about crime rates and the other variables of interest after the law was adopted but not before, therefore including these states doesn't really makes sense. Thus, we will drop the data for these states. We can use the `set_diff()` function of the `dplyr` package to see what states are in the `population_data` that contains all the original 51 states (recall this includes the District of Columbia) but are not in the `DONOHUE_DF`. The order matters here. If we did it the other way around with `population_data` listed second, then set_diff would test if there are any states in `Donohue_DF` that are not in `population_data`. As there are none this would result in nothing.

```{r}
baseline_year <- min(DONOHUE_DF$YEAR)
censoring_year <- max(DONOHUE_DF$YEAR)

DONOHUE_DF %<>%
  mutate(TIME_0 = baseline_year,
         TIME_INF = censoring_year) %>%
  filter(RTC_LAW_YEAR > TIME_0)

# DONOHUE_DF %<>% 
#   mutate(STATE = as.factor(STATE))
# 
# DONOHUE_DF %>% 
#   pull(STATE) %>% 
#   levels()

setdiff(distinct(population_data, STATE), 
        distinct(DONOHUE_DF, STATE))
```

We will also calculate a violent crime rate relative to the population in that state at that time, now that we have data for both crime count and population.  Will will also calculate the log value of this rate and the population.

```{r}
DONOHUE_DF %<>%
  mutate(Viol_crime_rate_1k = (Viol_crime_count*1000)/Population,
         Viol_crime_rate_1k_log = log(Viol_crime_rate_1k),
         Population_log = log(Population))
```



## **Mustard and Lott**
***

We will now bind the demographic data that we made for the Mustard and Lott analysis called `dem_Lott`, as well as all the other datasets that we have wrangled just as we did for the Donohue-like analysis. Again, this is possible because we have the same column names for each dataset. 

```{r}
LOTT_DF <- bind_rows(dem_LOTT,
                     ue_rate_data,
                     poverty_rate_data,
                     crime_data,
                     population_data,
                     ps_data) %>%
  pivot_wider(names_from = "VARIABLE",
              values_from = "VALUE") %>%
  left_join(RTC , by = c("STATE")) %>%
  mutate(RTC_LAW = case_when(YEAR >= RTC_LAW_YEAR ~ TRUE,
                              TRUE ~ FALSE)) %>%
   drop_na()


baseline_year <- min(LOTT_DF$YEAR)
censoring_year <- max(LOTT_DF$YEAR)

LOTT_DF %<>%
  mutate(TIME_0 = baseline_year,
         TIME_INF = censoring_year) %>%
  filter(RTC_LAW_YEAR > TIME_0)

setdiff(distinct(population_data, STATE), 
        distinct(LOTT_DF, STATE))

LOTT_DF %<>%
  mutate(Viol_crime_rate_1k = (Viol_crime_count*1000)/Population,
         Viol_crime_rate_1k_log = log(Viol_crime_rate_1k),
         Population_log = log(Population))

```
Let's see how the data compares:

We will check the dimensions of each using the base `dim()` function
```{r}
dim(LOTT_DF)
dim(DONOHUE_DF)
```

As expected the `Lott_DF` is 30 columns larger, due to the 30 additional demographic variables. We can check those now as well.

```{r}
LOTT_DF %>%
   colnames()

DONOHUE_DF %>%
   colnames()
```

Lastly, we will check that the `YEAR` values are the same. We can use the `setequal()` function of the `dplyr` package to see if the values are the same. 

```{r}
setequal(DONOHUE_DF %>% distinct(YEAR),
          LOTT_DF %>% distinct(YEAR))
```


Looks as expected! we have 

from Michael:
```{r, eval = FALSE}
LOTT_DF %>%
  group_by(YEAR) %>%
  tally() %>%
  filter(n != 51) %>%
  print(n=dim(.)[1])

summary(as.factor(LOTT_DF$STATE))

max(LOTT_DF$YEAR) - min(LOTT_DF$YEAR) + 1

LOTT_DF <- LOTT_DF %>%
  mutate(STATE = fct_collapse(STATE, "District of Columbia" = c("District of Columbia","D.C.")))

summary(as.factor(LOTT_DF$STATE))
  
length(levels(LOTT_DF$STATE))

LOTT_DF <- LOTT_DF %>%
  group_by(STATE, YEAR) %>%
  summarize_all(~na.omit(unique(.))) %>%
  ungroup() # This identifies unique observations, coalesces rows according to the grouping variable(s), and gets rid of of units that have incomplete data. This gives returns a dataframe with the most complete information.

summary(as.factor(LOTT_DF$STATE)) 

baseline_year <- min(LOTT_DF$YEAR)
censoring_year <- max(LOTT_DF$YEAR)

# Need to fix this to ensure severe bias is not introduced by prevalent "cases"

LOTT_DF <- LOTT_DF %>%
  mutate(TIME_0 = baseline_year,
         TIME_INF = censoring_year) %>%
  filter(RTC_LAW_YEAR > TIME_0)

LOTT_DF <- LOTT_DF %>%
  mutate(Viol_crime_rate_1k = (Viol_crime_count*1000)/Population,
         Viol_crime_rate_1k_log = log(Viol_crime_rate_1k),
         Population_log = log(Population))

summary(droplevels(as.factor(LOTT_DF$STATE)))

length(summary(droplevels(as.factor(LOTT_DF$STATE))))
```


Now we will save our wrangled data for the part 2 case study:

We can use the `here()` function of the `here` package to easily save this in a directory called `data` within the directory where are .Rproj file is located.

```{r, eval = FALSE}
save(LOTT_DF, DONOHUE_DF, file = here::here("data", "Wrangled_data.rda"))
```

# **Summary**
*** 

This case study has introduced many concepts for data importation and data wrangling. To continue with this data to see more about data analysis and visualization see this next [case study](https://www.opencasestudies.org/ocs-bp-RTC-analysis/).


# **Homework**
*** 

Ask students to import and wrangle similar datasets to those used here.


# **Additional Information**
***

## **Helpful Links**
***

[Tidyverse](https://www.tidyverse.org/){target="_blank"}  

The articles used to motivate this case study are:   
[Mustard and Lott](https://chicagounbound.uchicago.edu/cgi/viewcontent.cgi?article=1150&context=law_and_economics){target="_blank"}  
[Donohue, et al.](https://www.nber.org/papers/w23510.pdf){target="_blank"}     
[See here for a list of studies on this topic ](https://en.wikipedia.org/wiki/More_Guns,_Less_Crime){target="_blank"}  

<u>**Packages used in this case study:** </u>

Package   | Use in this case study                                                                        
---------- |-------------
[here](https://github.com/jennybc/here_here){target="_blank"}       | to easily load and save data
[readxl](https://readxl.tidyverse.org/){target="_blank"}      | to import the data in the excel files 
[readr](https://readr.tidyverse.org/){target="_blank"}      | to import the CSV file data
[pdftools](https://github.com/ropensci/pdftools){target="_blank"} | to import data from a pdf file
[dplyr](https://dplyr.tidyverse.org/){target="_blank"}      | to arrange/filter/select/compare specific subsets of the data  
[magrittr](https://cran.r-project.org/web/packages/magrittr/vignettes/magrittr.html){target="_blank"} | to use the compound assignment pipe operator `%<>%`
[tidyr](https://tidyr.tidyverse.org/){target="_blank"}      | to rearrange data in wide and long formats 
[stringr](https://stringr.tidyverse.org/articles/stringr.html){target="_blank"}    | to manipulate the character strings within the data  
[purrr](https://purrr.tidyverse.org/){target="_blank"}   | to import the data in all the different excel and csv files efficiently
[forcats](https://forcats.tidyverse.org/){target="_blank"}    | to allow for reordering of factors in plots
[tibble](https://tibble.tidyverse.org/){target="_blank"}     | to create data objects that we can manipulate with `dplyr`/`stringr`/`tidyr`/`purrr`

## **Session Info**
***

```{r}
sessionInfo()
```

## **Acknowledgments**
***

We would like to acknowledge [Daniel Webster](https://www.jhsph.edu/faculty/directory/profile/739/daniel-webster) for assisting in framing the major direction of the case study.

We would also like to acknowledge the [Bloomberg American Health Initiative](https://americanhealth.jhu.edu/) for funding this work. 

